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Kohli, Nidhi, and Amanda L. Sullivan. "Linear-linear piecewise growth mixture models with unknown random knots: A primer for school psychology." Journal of School Psychology 73 (April 2019): 89–100. http://dx.doi.org/10.1016/j.jsp.2019.03.004.
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Chilyabanyama, Obvious N., Roma Chilengi, Innocent Ngaruye, Najeeha Talat Iqbal, and Samuel Bosomprah. "Statistical Models for Estimating Linear Growth Velocity." International Journal of Nutrition, Pharmacology, Neurological Diseases 11, no. 4 (October 2021): 262–66. http://dx.doi.org/10.4103/ijnpnd.ijnpnd_6_21.
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Poor linear growth among infants is still a global public health issue. Linear growth velocity has been variously suggested as a more robust measure for growth over the classical measure of attained height for age. In this study, we systematically reviewed available literature for models used in estimating linear growth velocity. We searched Medline, Embase, Cochrane methodology register, Joanna Briggs Institute EBP, through the Ovid interface, and PubMed database to identify relevant articles that used statistical models to estimate linear growth velocity among infants. Longitudinal studies published in English were included. Two reviewers independently screened the titles and abstracts to identify potentially eligible studies. Any disagreements were discussed and resolved. Full-text articles were downloaded for all the studies that met the eligibility criteria. We synthesized literature using the preferred reporting items for systematic review and meta-analyses guidelines for the most used statistical methods for modelling infant growth trajectories. A total of 301 articles were retrieved from the initial search. Fifty-six full-text articles were assessed for eligibility and 16 of which were included in the final review with a total of 303,940 infants, median sample size of 732 (interquartile range: 241–1683). Polynomial function models were the most used growth model. Three (18.8%) of the articles modelled the linear growth. Two (12.5%) articles used mixed-effects models and another two (12.5%) used the Jenss-Bayley growth models to model linear growth. Other models included residual growth model, two-stage multilevel linear spline model, joint multilevel linear spline model, and generalized least squares with random effects. We have identified linear mixed-effects models, polynomial growth models, and the Jenss-Bayley model as the used models for characterizing linear growth among infants. Linear mixed-effects model is appealing for its robustness even under violation of largely robust even to quite severe violations of model assumptions.
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Quine, M. P., and J. S. Law. "Modelling random linear nucleation and growth by a Markov chain." Journal of Applied Probability 36, no. 1 (March 1999): 273–78. http://dx.doi.org/10.1239/jap/1032374248.
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In an attempt to investigate the adequacy of the normal approximation for the number of nuclei in certain growth/coverage models, we consider a Markov chain which has properties in common with related continuous-time Markov processes (as well as being of interest in its own right). We establish that the rate of convergence to normality for the number of ‘drops’ during times 1,2,…n is of the optimal ‘Berry–Esséen’ form, as n → ∞. We also establish a law of the iterated logarithm and a functional central limit theorem.
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Quine, M. P., and J. S. Law. "Modelling random linear nucleation and growth by a Markov chain." Journal of Applied Probability 36, no. 01 (March 1999): 273–78. http://dx.doi.org/10.1017/s0021900200017034.
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In an attempt to investigate the adequacy of the normal approximation for the number of nuclei in certain growth/coverage models, we consider a Markov chain which has properties in common with related continuous-time Markov processes (as well as being of interest in its own right). We establish that the rate of convergence to normality for the number of ‘drops’ during times 1,2,…n is of the optimal ‘Berry–Esséen’ form, as n → ∞. We also establish a law of the iterated logarithm and a functional central limit theorem.
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Kumar, Amit, Sanjeev Kumar, Manjari Pandey, Chirag Chaudhari, Med Ram Verma, Chandrahas, and Anuj Chauhan. "Bertalanffy Model Reflects Growth Trajectory in Aseel Chicken." Indian Journal of Veterinary Sciences & Biotechnology 18, no. 5 (November 7, 2022): 59–62. http://dx.doi.org/10.48165/ijvsbt.18.5.12.
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Aseel, a popular breed of native chicken, characterized by its pugnacity, fighting strength and royal gait is being used to create crosses for domestic chicken production. However, information on its growth models is scanty. An experiment was conducted to evaluate different non-linear models and to find out best fitting model in Aseel, being maintained at Central Avian Research Institute, Izatnagar, Bareilly. Data on body weights from 12-weeks of age to 20-weeks of age at biweekly intervals were recorded on a random bred single-hatched flock. Owing to the non-linear characteristic of growth, three non-linear models namely, Gompertz, Bertalanffy and Logistic models were evaluated. Goodness of fit for all the models were checked using coefficient of determination (R2), adjusted coefficient of determination (Adj-R2), mean square error (MSE), mean absolute error (MAE) and Akaike information criterion (AIC). The Bertalanffy model most accurately characterized the growth trend in males, females and pooled sex data. The study revealed that this model may be used to ascertain the average body weights in Aseel chicken under random mating. The investigation has generated baseline data on growth modelling of random bred groups and may be used in similar investigations on other native chicken breeds.
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Sarmento, José Lindenberg Rocha, Robledo de Almeida Torres, Wandrick Hauss de Sousa, Lucia Galvão de Albuquerque, Raimundo Nonato Braga Lôbo, and José Ernandes Rufino de Sousa. "Modeling of average growth curve in Santa Ines sheep using random regression models." Revista Brasileira de Zootecnia 40, no. 2 (February 2011): 314–22. http://dx.doi.org/10.1590/s1516-35982011000200012.
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Polynomial functions of age of different orders were evaluated in the modeling of the average growth trajectory in Santa Ines sheep in random regression models. Initially, the analyses were performed not considering the animal effect. Subsequently, the random regression analyses were performed including the random effects of the animal and its mother (genetic and permanent environment). The linear fit was lower, and the other orders were similar until near 100 days of age. The cubic function provided the closest fit of the observed averages, mainly at the end of the curve. Orders superior to this one tended to present incoherent behavior with the observed weights. The estimated direct heritabilities, considering the linear fit, were higher to those estimated by considering other functions. The changes in animal ranking based on predicted breeding values using linear fit and superior orders were small; however, the difference in magnitude of the predicted breeding values was higher, reaching values 77% higher than those obtained with the cubic function. The cubic polynomial function is efficient in describing the average growth curve.
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Li, Yao Xiang, and Li Chun Jiang. "Modeling Microfibril Angle of Larch Using Linear Mixed-Effects Models." Advanced Materials Research 267 (June 2011): 516–20. http://dx.doi.org/10.4028/www.scientific.net/amr.267.516.
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Earlywood microfibril angle (MFA) was determined at each growth ring from disks at breast height (1.3 m) from 6 dahurian larch (Larix gmelinii. Rupr.) trees grown in northeastern China. Significant variation in microfibril angle was observed among growth rings. MFA at breast height varied from 7.5°to 21.5°between growth rings and showed a descreasing trend from pith to bark for each tree. A second order polynomial equation with linear mixed-effects was used for modeling earlywood MFA. The LME procedure in S-Plus is used to fit the mixed-effects models for the MFA data. The results showed that the polynomial model with three random parameters could significantly improve the model performance. The fitted mixed-effects model was also evaluated using a separate dataset. The mixed model was found to predict MFA better than the original model fitted using ordinary least-squares based on absolute and relative errors.
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Codling, Edward A., Michael J. Plank, and Simon Benhamou. "Random walk models in biology." Journal of The Royal Society Interface 5, no. 25 (April 15, 2008): 813–34. http://dx.doi.org/10.1098/rsif.2008.0014.
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Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce the mathematics behind random walks in a straightforward manner and to explain how such models can be used to aid our understanding of biological processes. We introduce the mathematical theory behind the simple random walk and explain how this relates to Brownian motion and diffusive processes in general. We demonstrate how these simple models can be extended to include drift and waiting times or be used to calculate first passage times. We discuss biased random walks and show how hyperbolic models can be used to generate correlated random walks. We cover two main applications of the random walk model. Firstly, we review models and results relating to the movement, dispersal and population redistribution of animals and micro-organisms. This includes direct calculation of mean squared displacement, mean dispersal distance, tortuosity measures, as well as possible limitations of these model approaches. Secondly, oriented movement and chemotaxis models are reviewed. General hyperbolic models based on the linear transport equation are introduced and we show how a reinforced random walk can be used to model movement where the individual changes its environment. We discuss the applications of these models in the context of cell migration leading to blood vessel growth (angiogenesis). Finally, we discuss how the various random walk models and approaches are related and the connections that underpin many of the key processes involved.
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Hay, El Hamidi. "Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population." Animals 14, no. 20 (October 18, 2024): 3014. http://dx.doi.org/10.3390/ani14203014.
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The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models—Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons—for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models—GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B. The results demonstrated that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited a superior prediction accuracy for WW. Furthermore, GBLUP outperformed the other models in terms of model fit, as evidenced by the lower mean square error values and regression coefficients of the corrected phenotypes on predicted values. Overall, the GBLUP model delivered a superior prediction accuracy and model fit compared to the machine learning models tested.
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Wang, Weibo. "Forecasting The Population of China From 2020 To 2025 Based on Random Forest and Linear Regression." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 511–18. http://dx.doi.org/10.54097/a70zsh28.
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The population dynamics of a country do play a vital role in its economic and political development. The COVID-19 epidemic has significantly affected the world's population. However, most people only care about the negative influence caused by COVID-19 but ignore the positive influence. This article uses two machine learning models, the random forest model, and the linear regression model, to predict the population change in China if there were no COVID-19 pandemic. With the predicted results, this article can compare the potential positive impact of the pandemic. This paper tries to fit two popular models, namely, Random Roest and Linear Regression to forecast the population of China from 2020 to 2025. The historical birth rate, death rate, and GDP growth rate of China are collected as features adding to the models to decline the error. For the Random Forest model, this paper set up an ensemble of decision trees to predict the future population of China. For the Linear Regression model, our features and population fit a linear connection. The findings of two models are compared in this article, and it is suggested that the random forest model is more suited for population forecasting. In addition, according to this study, the COVID-19 pandemic has some favorable effects on economic growth and birth rates. The article emphasizes the need to not only focus on the negative effects of the pandemic. Furthermore, the article points out that the linear regression model has poor fitting results for non-linear relationships. It suggests exploring more non-linear models for prediction and considering more influential parameters.
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Böhm, Volker, and Jan Wenzelburger. "PERFECT PREDICTIONS IN ECONOMIC DYNAMICAL SYSTEMS WITH RANDOM PERTURBATIONS." Macroeconomic Dynamics 6, no. 5 (September 26, 2002): 687–712. http://dx.doi.org/10.1017/s1365100501010136.
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The paper studies multivariate non linear economic dynamical systems with an expectations feedback subjected to exogenous perturbations. In these systems, agents form expectations on future variables based on subjective transition probabilities given by a Markov kernel. The notion of a perfect Markov kernel that generates rational expectations along all orbits of the system is proposed. Conditions are provided under which perfect Markov kernels exist. Applications are given to models of the Cobweb type, to multivariate affine-linear systems, and to the stochastic OLG model of economic growth. For the latter two models, it is shown when a globally attracting random fixed point with rational expectations exists.
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Terra, Marcela de Castro Nunes Santos, Marcos Gabriel Braz de Lima, Juliano de Paulo dos Santos, Natielle Gomes Cordeiro, Kelly Marianne Guimarães Pereira, Daniel Dantas, Natalino Calegario, and Soraya Alvarenga Botelho. "Non-linear growth models for tree species used for forest restoration in Brazilian Amazon Arc of Deforestation." Pesquisa Florestal Brasileira 42 (June 13, 2022): 1–13. http://dx.doi.org/10.4336/2022.pfb.42e202102180.
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The large amount of degraded areas and productive potential of the legal reserves in Brazil make restoration an environmental demand and a commercial opportunity. We modelled the diameter growth as a function of age of eight tree species in restoration plantations in the Brazilian Amazon. From 14 years of annual forest inventory data, for each species, we tested variations of logistic function: simple logistic, logistic with covariant (plant area at the time of planting), logistic with random effect, logistic with random effect and covariant. Amongst the studied species, Schizolobium parahyba var. amazonicum, Tectona grandis and Simarouba amara showed the highest growth rates while Cordia alliodora, Cedrela odorata and three species of the genus Handroanthus showed slower growth. The gains from using the covariant in modeling were small for both fixed and mixed-effect models. Gains from the inclusion of the random effect were substantial. Mixed-effect models had the best performance in modeling the growth of the species. Our results provide basis for a critical view of the criteria and possibilities for degraded areas restoration and management practices in legal reserves of the Amazon. An economic analysis is required to ensure the viability of these areas’ sustainable exploitation.
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Hirsch, C., D. Neuhäuser, C. Gloaguen, and V. Schmidt. "First Passage Percolation on Random Geometric Graphs and an Application to Shortest-Path Trees." Advances in Applied Probability 47, no. 2 (June 2015): 328–54. http://dx.doi.org/10.1239/aap/1435236978.
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We consider Euclidean first passage percolation on a large family of connected random geometric graphs in the d-dimensional Euclidean space encompassing various well-known models from stochastic geometry. In particular, we establish a strong linear growth property for shortest-path lengths on random geometric graphs which are generated by point processes. We consider the event that the growth of shortest-path lengths between two (end) points of the path does not admit a linear upper bound. Our linear growth property implies that the probability of this event tends to zero sub-exponentially fast if the direct (Euclidean) distance between the endpoints tends to infinity. Besides, for a wide class of stationary and isotropic random geometric graphs, our linear growth property implies a shape theorem for the Euclidean first passage model defined by such random geometric graphs. Finally, this shape theorem can be used to investigate a problem which is considered in structural analysis of fixed-access telecommunication networks, where we determine the limiting distribution of the length of the longest branch in the shortest-path tree extracted from a typical segment system if the intensity of network stations converges to 0.
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Hirsch, C., D. Neuhäuser, C. Gloaguen, and V. Schmidt. "First Passage Percolation on Random Geometric Graphs and an Application to Shortest-Path Trees." Advances in Applied Probability 47, no. 02 (June 2015): 328–54. http://dx.doi.org/10.1017/s0001867800007886.
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We consider Euclidean first passage percolation on a large family of connected random geometric graphs in the d-dimensional Euclidean space encompassing various well-known models from stochastic geometry. In particular, we establish a strong linear growth property for shortest-path lengths on random geometric graphs which are generated by point processes. We consider the event that the growth of shortest-path lengths between two (end) points of the path does not admit a linear upper bound. Our linear growth property implies that the probability of this event tends to zero sub-exponentially fast if the direct (Euclidean) distance between the endpoints tends to infinity. Besides, for a wide class of stationary and isotropic random geometric graphs, our linear growth property implies a shape theorem for the Euclidean first passage model defined by such random geometric graphs. Finally, this shape theorem can be used to investigate a problem which is considered in structural analysis of fixed-access telecommunication networks, where we determine the limiting distribution of the length of the longest branch in the shortest-path tree extracted from a typical segment system if the intensity of network stations converges to 0.
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Přibyl, J., H. Krejčová, J. Přibylova, I. Misztal, S. Tsuruta, and N. Mielenz. "Models for evaluation of growth of performance tested bulls." Czech Journal of Animal Science 53, No. 2 (February 7, 2008): 45–54. http://dx.doi.org/10.17221/331-cjas.
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Before being used for insemination, young bulls of Czech Fleckvieh (CF) are tested for growth at performance-test stations. While at stations, the bulls are weighed monthly. Evaluation included 7 448 bulls with 82 676 records of weight measured from 6 to 520 days of life. In the station-year-period (HYS), which can be prolonged up to 3 months, different groups were tested according to the beginning of growth curve and according to test-days of weighing. Weight analyses were used to handle heterogeneous variability based on age. Legendre Polynomials (LP) with 5 parameters described the average growth curve for HYS classes. Deviations from average curves were decomposed into genetic (G), animal’s permanent environment (PE) and residual (RES) components. Functions of (G) and (PE) were tested using LP random regression (RR) methodology with 5 or 3 parameters and Linear Spline (SP) function with 5 knots. Variance increases with the age of the animals. From 100 to 400 days, heritability was nearly the same with a mild depression in the middle of the period. The average was <I>h</I><sup>2</sup> = 0.31 and ended with <I>h</I><sup>2</sup> = 0.36. Results were similar for variance components, heritability, genetic, environmental and phenotype correlations from different models with different LP and SP functions. Higher RES variability occurred only for LP with 3 parameters. For traits like live weight, the RR should have at least 3 parameters and SP function should be used.
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Rovine, Michael J., and Peter C. M. Molenaar. "A Nonstandard Method for Estimating a Linear G rowth Model in LISREL." International Journal of Behavioral Development 22, no. 3 (September 1998): 453–73. http://dx.doi.org/10.1080/016502598384225.
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A structural equation modelling approach for estimating a linear growth curve model is presented. This method can be used for a wide variety of models based on the General Linear Mixed Model. This model is a simple example of a multilevel or random coefficients model. The logic of the method is described, and then a LISR E L implementation is presented. A s an example of the individual linear growth curve model, growth data presented by Pothoff and Roy (1964) is analysed.
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Huang, Yitong. "Comparison Of 6 Machine Learning Models in Estimating Population Growth." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 519–23. http://dx.doi.org/10.54097/h97nwj92.
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With the rapidly increasing population globally, it is essential for policymakers to be able to accurately predict or gain an idea of the forecast of population growth to be able to make effective regulations that can benefit the general public. Therefore, the development of a machine learning model to estimate future population growth is crucial. In this article, various machine learning models such as linear regression, logistic regression, decision trees, random forest, neural networks, and support vector machines are discussed and the benefits and downsides of each are considered. Factors impacting population growth are also discussed to conclude the qualities needed for a model to most suitably perform the task of population prediction. In the end, it is shown that random forest is the best model for this job as it can give a generalized pattern for its results as well as handle complex data types. This paper provides predictions and insights based on machine learning to predict future demographic trends, which can provide useful information for policymakers, researchers, and society in various fields.
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Castro, G. C., J. E. G. Campelo, J. L. R. Sarmento, M. D. F. Carvalho, D. H. Cavalcante, and L. A. S. Fiqueiredo Filho. "Random regression models for the evaluation of the growth of goats of the Anglonubian breed." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 72, no. 3 (May 2020): 961–69. http://dx.doi.org/10.1590/1678-4162-11501.
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ABSTRACT A total of 6593 weight records collected from 796 male and female Anglo-Nubian goats aged up to 130 days, offspring from 29 sires and 225 dams, were used to compare models and estimate genetic parameters throughout the growth curve by applying random regression models. Direct and maternal additive genetic effects and direct and maternal permanent environmental effects were included as random in the models. The contemporary groups were included as fixed effects and goat age at kidding was included as a covariable (linear and quadratic). The choice of the best model was based on the AIC, BIC and AICc criteria. Variance estimates of the four random effects increased as the animals aged. Direct heritability (h2) rose from 0.13 to 0.40 with age, whereas maternal heritability showed a low value. Genetic correlations of weight between closer ages were high. The most suitable random regression model to compare the fitting of random effects was that which employed the Legendre polynomials of quadratic order with homogeneous variance (3333-1).
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Qamar, Huma, Ulaina Tariq, Diego Bassani, Akpevwe Onoyovwi, Abdullah Al Mahmud, Tahmeed Ahmed, Robert Bandsma, and Daniel Roth. "Discrepant Inferences When Modeling Associations Between Time-Varying Exposures and Linear Growth Trajectories in Infancy Using Length-For-Age Z Scores Versus Raw Length." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 1059. http://dx.doi.org/10.1093/cdn/nzaa054_131.
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Abstract Objectives To compare inferences from longitudinal models of the relationships between biomarkers of interest and linear growth outcomes in infancy using length-for-age z-scores (LAZ) based on age- and sex-specific growth standards, or raw length. Methods This was a secondary analysis of data from a study of the association between bone-related biomarkers and infant linear growth trajectories up to 1 year of life in a subset of infants (n = 820) enrolled in the Maternal Vitamin D for Infant Growth trial. The linear growth outcome (LAZ or raw length) was modelled as a function of the interaction between each biomarker and age using linear mixed effect models with restricted cubic splines. Models were specified to obtain the best fit and reconcile discrepancies in results from LAZ and length models. Inferences from marginal effects at birth, 3 months, 6 months, and 12 months were compared, for a total 4 effect estimates from each of 10 pairs of LAZ and length models, resulting in 40 pairs of estimates. The following biomarkers were included: fibroblast growth factor 21 (FGF21), fibroblast growth factor 23 (FGF23), N-terminal propeptide of C-type natriuretic peptide (NT-proCNP), osteocalcin, osteoprotegerin, receptor activator of nuclear activator kappa-b ligand (RANKL), 25-hydroxyvitamin D (25OHD), C-reactive protein (CRP), Interleukin 6 (IL6), and insulin-like growth factor-1 (IGF1). Biomarkers were time-varying, measured in cord blood and at 3 and 6 months of age. Results The best fitting model for LAZ had 3 knots with random slopes, and the best fitting model for raw length had 5 knots, random slopes, and an exponential residual covariance structure. Comparisons of the pairs of marginal estimates from the LAZ vs length models resulted in discrepant inferences for 25% of effect estimates (10/40). Results were consistently concordant only for FGF23, 25OHD, CRP, and IL6. Conclusions Length and LAZ represent the same biological construct but their use in longitudinal modelling may lead to different inferences about associations between time-varying exposures and infant growth, possibly due to residual confounding by age. These findings raise concerns about the reliability of studies of determinants or markers of infant linear growth based on longitudinal modelling of growth trajectories. Funding Sources Bill and Melinda Gates Foundation.
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Iwaisaki, H., S. Tsuruta, I. Misztal, and J. K. Bertrand. "Genetic parameters estimated with multitrait and linear spline-random regression models using Gelbvieh early growth data1." Journal of Animal Science 83, no. 4 (April 1, 2005): 757–63. http://dx.doi.org/10.2527/2005.834757x.
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Ganjali, Mojtaba, Taban Baghfalaki, and Adeniyi Francis Fagbamigbe. "A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models." Afrika Statistika 15, no. 3 (June 1, 2020): 2387–93. http://dx.doi.org/10.16929/as/2020.2387.164.
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Growth curve data consist of repeated measurements of a continuous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analysing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. In this paper, a sensitivity analysis of using different parametric and non-parametric distributions for the random effects on the results of applying non-linear mixed models is proposed for emphasizing the possible heterogeneity in the population. A Bayesian MCMC procedure is developed for parameter estimation and inference is performed via a hierarchical Bayesian framework. The methodology is illustrated using a real data set on study of influence of menarche on changes in body fat accretion.
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Giolo, Suely Ruiz, Robin Henderson, and Clarice Garcia Borges Demétrio. "Mixed-effects growth curves in the valuation of Nellore sires." Scientia Agricola 66, no. 1 (February 2009): 84–89. http://dx.doi.org/10.1590/s0103-90162009000100012.
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Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.
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Wasserfallen, W. "Trends, Random Walks and the Expectations-Augmented Phillips-Curve- A Summary." Recherches économiques de Louvain 51, no. 3-4 (December 1985): 387–88. http://dx.doi.org/10.1017/s0770451800082695.
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In most macroeconomic models, variations in nominal variables, such as inflation or money growth, are considered to be important determinants of cyclical fluctuations in real activity. The major hypothesis in that respect is the so-called Phillips-curve. In its modern interpretation, it maintains that only unexpected changes in nominal magnitudes produce real effects. Reliable empirical evidence on these effects is therefore crucial for the building of macroeconomic models and the conduct of monetary policy.Time series of output, industrial production or employment however contain growth and seasonal components in addition to cyclical elements. The empirical implementation of business cycle models therefore requires assumptions with respect to growth and seasonal parts as well, in order to isolate cyclical movements and to avoid misspecified equations. It has become general practice to assume that economic growth can be reasonably well approximated by a deterministic linear time trend. Seasonality is either captured through the explicit introduction of dummy variables or the use of seasonally adjusted data. Again, these procedures assume a deterministic seasonal structure. It is generally concluded in this literature, that unanticipated and possibly also expected changes in nominal magnitudes have non-negligible real effects.
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Singer, Judith D. "Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models." Journal of Educational and Behavioral Statistics 23, no. 4 (December 1998): 323–55. http://dx.doi.org/10.3102/10769986023004323.
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SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistical package makes it an ideal choice for empirical researchers and applied statisticians seeking to do data reduction, management, and analysis within a single statistical package. Because the program was developed from the perspective of a “mixed” statistical model with both random and fixed effects, its syntax and programming logic may appear unfamiliar to users in education and the social and behavioral sciences who tend to express these models as multilevel or hierarchical models. The purpose of this paper is to help users familiar with fitting multilevel models using other statistical packages (e.g., HLM, MLwiN, MIXREG) add SAS PROC MIXED to their array of analytic options. The paper is written as a step-by-step tutorial that shows how to fit the two most common multilevel models: (a) school effects models, designed for data on individuals nested within naturally occurring hierarchies (e.g., students within classes); and (b) individual growth models, designed for exploring longitudinal data (on individuals) over time. The conclusion discusses how these ideas can be extended straighforwardly to the case of three level models. An appendix presents general strategies for working with multilevel data in SAS and for creating data sets at several levels.
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Dima, Bogdan, and Ştefana Maria Dima. "Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries." Journal of Heterodox Economics 4, no. 2 (December 1, 2017): 51–80. http://dx.doi.org/10.1515/jheec-2017-0004.
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Abstract This paper explores the tax burden - economic growth nexus. It advances an explanatory framework for the existence of such nexus. First, we argue that tax burden reduces the income remaining at the disposal of the private sector. Second, we empirically test for the existence of a non-linear impact of corporate tax burden on growth for a dataset of 21 OECD countries, for the period 1975 - 2012. We mostly involve mixed effects models with three levels of nested random effects. Our main empirical result consists in the evidences of a non-linear relation between corporate tax burden and economic growth.
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Cue, R. I., D. Pietersma, D. Lefebvre, R. Lacroix, K. Wade, D. Pellerin, A.-M. de Passillé, and J. Rushen. "Growth modeling of dairy heifers in Québec based on random regression." Canadian Journal of Animal Science 92, no. 1 (March 1, 2012): 33–47. http://dx.doi.org/10.4141/cjas2011-083.
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Cue, R. I., Pietersma, D., Lefebvre, D., Lacroix, R., Wade, K., Pellerin, D., de Passillé, A-M. and Rushen, J. 2012. Growth modeling of dairy heifers in Québec based on random regression. Can. J. Anim. Sci. 92: 33–47. A total of 144 006 weight (calculated from tape girth measurements) and height data records from Québec dairy heifers were analyzed using random regression to estimate growth curve parameters of Ayrshires, Brown Swiss and Holstein animals to permit prediction of individual heifer growth from 0 to 32 mo. There were, on average, 5.15 records per heifer (minimum 3 records, maximum 25 records). The body weight data were analyzed using linear and quadratic fixed and random regressions, with a power-of-the-mean (POM) function to model the residual variance. The POM was 1.2 for Holstein and Ayrshire and slightly less than 1 for Brown Swiss. Estimated body weight at 24 mo was 507, 564, 624 kg, for Ayrshires, Brown Swiss and Holstein, respectively. The height data were analyzed with a Brody, monomolecular non-linear growth curve model. Mature height was estimated to be 148 cm in both Holstein and Ayrshires, and 150 cm in Brown Swiss. Random regression models were shown to be able to predict individual growth, and can be incorporated in decision-support tools to help producers reducing the average age at first calving.
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Wang, Mingliang, Jagtar Bhatti, Yonghe Wang, and Thierry Varem-Sanders. "Examining the Gain in Model Prediction Accuracy Using Serial Autocorrelation for Dominant Height Prediction." Forest Science 57, no. 3 (June 1, 2011): 241–51. http://dx.doi.org/10.1093/forestscience/57.3.241.
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Abstract Within-subject serial correlation (autocorrelation) has long been a concern in forest growth and yield modeling but has been ignored for predictive purposes in most studies. In this study, we used linear prediction theory combined with linearized (with respect to random effects) nonlinear mixed models to investigate the improvement in model prediction achieved with autocorrelation. In this setting, predictions rely on estimates of common parameters obtained from a set of previous growth series and prior observations of new growth series, allowing the response variable for the new series to be projected either backward or forward in time. The prediction gains associated with using autocorrelation were evaluated using stem analysis data sets for black spruce (Picea mariana [Mill.] BSP) and red alder (Alnus rubra Bong.). The evaluations involved splitting the data and comparing models with one or more random parameters, with and without use of autocorrelation. Autocorrelation improved the projection of dominant height (site index) over short ranges (10–20 years), but the gain was trivial for the long range (>20 years). Consequently, in cases of dominant height projection based on one single observation, for practical purposes, autocorrelation can be ignored in both model-fitting and prediction stages. Cross-comparison between models with different random effects indicated that simple models with one random effect had the best predictive performance. Rather than excluding such models solely on the basis of certain fit statistics, it is recommended that the predictive abilities of models with a single random effect be evaluated, with and without correlated errors, relative to their counterparts with more random effects.
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Liu, Yuxin. "Machine Learning-Based Prediction of Carbon Dioxide Emissions from Automobiles and Influencing Factors." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 80–86. http://dx.doi.org/10.54097/4c4a2g08.
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In recent years, the number of automobiles has been increasing globally, further leading to an increase in carbon dioxide emissions year after year. Carbon dioxide emissions from automobiles have become an important factor in global climate change and have attracted global attention. Firstly, the data were analyzed for missing values all duplicates were removed, and the main discrete random variables were converted into continuous random variables; then, the correlation coefficients of Pearson correlation coefficients were used to analyze the correlation between each automobile characteristic, and heat maps were drawn to remove the influencing factors with weak correlation; finally, the prediction models were established based on multiple linear regression and the Random Forest method, respectively. The results show that in both models, Fuel Consumption Comb is the most important factor influencing the growth of automobile carbon dioxide emissions; the random forest model is better than the multiple linear regression model and can effectively predict automobile carbon dioxide emissions.
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Silveira, Maurício Vargas da, Júlio César de Souza, Tássia Souza Bertipaglia, Paulo Bahiense Ferraz Filho, Mariana Alencar Pereira, and Carlos Henrique Cavallari Machado. "Growth curves and genetic parameters in Nelore animals estimated by random regression models." Semina: Ciências Agrárias 40, no. 2 (April 15, 2019): 935. http://dx.doi.org/10.5433/1679-0359.2019v40n2p935.
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The objective of this work was to estimate growth curves and genetic parameters from birth to 650 days of age of Nelore cattle raised in pasture in two production regions of the Mato Grosso do Sul State, Brazil (233,835 weight records from 47,459 cattle were analyzed). Genetic parameters were determined by random regression using Legendre orthogonal polynomials of cubic order, and age at weighing was considered in the model as a fixed effect to model the average growth trajectory. In the models, the effects of the contemporary group were considered as fixed and, as covariates, the animal age at weighing and the cow age at calving were nested in the animal age class (linear and quadratic effects), forming eight age classes. All models included the direct genetic additive, maternal genetic, and animal permanent environment as random effects, and the most appropriate model to describe the studied effects was defined according to the AIC and BIC criteria. Heritability estimates for birth weight varied between the two production regions, Campo Grande-Dourados (R1) and Alto Taquari-Bolsão (R2) and R1 (0.36 ± 0.02) and R2 (0.28 ± 0.03), and there were variations in the estimates at advanced ages. In both regions, the highest heritability values at 650 days of age were 0.47 ± 0.03 and 0.65 ± 0.02 for R1 and R2, respectively, with high heritability reflecting the high values of additive genetic variance. The random regression methodology was efficient in estimating growth curves and genetic parameters. Growth curves were different when they were estimated separately by sex, birth season, and production region. Genetic parameters estimated separately by region indicate differences in additive genetic variance, maternal additive, and animal permanent environment for weights up to 650 days of age.
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Yilmaz, Ilker, and Ahmed Samour. "The Effect of Cash Holdings on Financial Performance: Evidence from Middle Eastern and North African Countries." Journal of Risk and Financial Management 17, no. 2 (January 30, 2024): 53. http://dx.doi.org/10.3390/jrfm17020053.
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This work aimed to examine the effect of corporate cash holdings on financial performance. The data covered 536 non-financial firms for the 2006–2020 period from 11 MENA region countries. This study used fixed- and random-effects testing models. To the best of the authors’ knowledge, this is the first study that aimed to study the effect of corporate cash holdings on financial performance in MENA countries in two aspects: linear and non-linear relationships. By using the return on assets, return on equity, earnings before interest, and the tax margin as the indicators of financial performance, we developed two groups of models investigating the linear and non-linear relationships between cash holdings and profitability measures. The models included several control variables, namely leverage, firm size, sales growth rate, tangibility, dividend pay-out ratio, and gross domestic product (GDP) growth rate. The results of this study revealed that both the linear and non-linear models produced significant results for the return on assets and the return on equity, but for the earnings before interest and tax margins, the linear model was insignificant. The non-linear models indicated an optimal level of cash holdings. In this context, the policymakers must actively evaluate these policies, such as working capital management and its effect on financial performance. In addition, the policymakers must consider macroeconomic conditions when designing corporate cash-holding policies.
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CACERES, MANUEL O. "RANDOM EVOLUTION IN POPULATION DYNAMICS." International Journal of Bifurcation and Chaos 20, no. 02 (February 2010): 297–307. http://dx.doi.org/10.1142/s0218127410025740.
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We present a perturbative formalism to deal with linear random positive maps. We generalize the biological concept of the population growth rate when a Leslie matrix has random elements (i.e. characterizing the macroscopic disorder in the vital parameters). The dominant eigenvalue of which defines the asymptotic dynamics of the mean-value population vector state, is presented as the effective growth rate of a random Leslie model. The problem was reduced to the calculation of the smallest positive root [Formula: see text] of the secular polynomial appearing in the general expression for the mean-value Green function 〈G(z)〉. This nontrivial polynomial can be obtained order by order in terms of a diagrammatic technique built with Terwiel's cumulants, which have carefully been identified in the present work. By understanding how this smallest positive root [Formula: see text] depends on the model of disorder, one can link the asymptotic population dynamics with the statistical properties of the errors (mutations) in the vital parameters. This eigenvalue has the meaning of an effective Perron–Frobenious eigenvalue for a random positive matrix. Analytical (exact and perturbative calculations) results are presented for several models of disorder.
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Barnabani, Marco. "A Parametric Test to Discriminate Between a Linear Regression Model and a Linear Latent Growth Model." International Journal of Statistics and Probability 6, no. 3 (May 14, 2017): 157. http://dx.doi.org/10.5539/ijsp.v6n3p157.
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In longitudinal studies with subjects measured repeatedly across time, an important problem is how to select a model generating data by choosing between a linear regression model and a linear latent growth model. Approaches based both on information criteria and asymptotic hypothesis tests of the variances of ''random'' components are widely used but not completely satisfactory. We propose a test statistic based on the trace of the product of an estimate of a variance covariance matrix defined when data come from a linear regression model and a sample variance covariance matrix. We studied the sampling distribution of the test statistic giving a representation in terms of an infinite series of generalized F-distributions. Knowledge about this distribution allows us to make inference within a classical hypothesis testing ramework. The test statistic can be used by itself to discriminate between the two models and/or, if duly modified, it can be used to test randomness on single components. Moreover, in conjunction with some model selection criteria, it gives additional information which can help in choosing the model.
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Brown, Schyler, Lana L. Narine, and John Gilbert. "Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama." Remote Sensing 14, no. 11 (June 4, 2022): 2708. http://dx.doi.org/10.3390/rs14112708.
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Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary.
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Ruškić, Nenad, Valentina Mirović, Milovan Marić, Lato Pezo, Biljana Lončar, Milica Nićetin, and Ljiljana Ćurčić. "Model for Determining Noise Level Depending on Traffic Volume at Intersections." Sustainability 14, no. 19 (September 29, 2022): 12443. http://dx.doi.org/10.3390/su141912443.
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The negative external effects caused by traffic growth have been recognized as the main factors that degrade city quality of life. Therefore, research around the world is being conducted to understand the impact of traffic better and find adequate measures to reduce the negative impact of traffic growth. The central part of this research consists of mathematical models for simulating the negative consequences of congestion and noise pollution. Four non-linear models for determining noise levels as a function of traffic flow parameters (intensity and structure) in the urban environment were developed. The non-linear models, including two artificial neural networks and two random forest models, were developed according to the experimental measurements in Novi Sad, Serbia, in 2019. These non-linear models showed high anticipation accuracy of the equivalent continuous sound level (Laeq), with R2 values of 0.697, 0.703, 0.959 and 0.882, respectively. According to the developed ANN models, global sensitivity analysis was performed, according to which the number of buses at crossings was the most positively signed influential parameter in Laeq evaluation, while the lowest Laeq value was reached during nighttime. The locations occupied by frequent traffic such as Futoska and Temerinska positively influenced the Laeq value.
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Vostrý, L., Z. Veselá, and J. Přibyl. "Genetic parameters for growth of young beef bulls." Archives Animal Breeding 55, no. 3 (October 10, 2012): 245–54. http://dx.doi.org/10.5194/aab-55-245-2012.
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Abstract. The average daily gains of young bulls on test stations (ADGT) were analysed for the most frequent breeds of beef cattle in the Czech Republic using a multiple-trait animal model. Body weights at birth (W0), at 120 days of age (W120) and at weaning at 210 days (WW) were considered in this model as pre-weaning growth. The tested models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent animal environment effect, permanent maternal environment effect, and some of the fixed effects: dam’s age, sex, herd-year-season, linear and quadratic regression on age at the beginning of the test. For optimization of the models Akaike information criterion (AIC), residual variance and likelihood ratio test were used. Coefficients of direct and maternal heritability across breeds of about 0.25 for W0, about 0.17 for W120, about 0.17 for WW and about 0.29 for ADGT were estimated by all models. All criteria selected models including the permanent animal environment effect, which was the most important effect in the model.
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Zhang, Chang, and Qisen Cheng. "Predicting Melt-Crystal Interface Position and Shape during the Manufacturing Process of Single Crystal via Explainable Machine Learning Models." IOP Conference Series: Materials Science and Engineering 1258, no. 1 (October 1, 2022): 012029. http://dx.doi.org/10.1088/1757-899x/1258/1/012029.
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In recent years, employing machine learning models to predict the process parameters during the manufacturing process of single crystals has gained wide attention as it’s reliable and much faster than traditional numerical simulation approaches. However, most machine learning models used in previous studies are black box models, which don’t provide explainable results. In this paper, we present a feasibility study of applying explainable machine learning models to predict steady-state melt-crystal interface position and deflection with the set-point temperature of 5 heaters in a vertical Bridgman furnace. The dataset used to train and evaluate the machine learning models was generated by 2-D numerical simulation. We experimented with linear regression and random forest algorithms, and then used linear regression coefficient and SHAP value to quantify the impact of each input on the output, from which we inferred a heater control strategy that could potentially improve the crystal growth process. Our encouraging results show that explainable machine learning models can be applied to predict crystal growth process parameters in real-time and generate actionable insights to guide crystal manufacturing practice.
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F. AL- Saadony, Muhannad. "Models of Anomalous Diffusion and its analysis." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ) 56, no. 1 (January 8, 2025): 506–14. https://doi.org/10.55562/jrucs.v56i1.45.
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The anomalous diffusion is characterized by deviation from Gaussian statistics and the absence of a linear time dependency in the mean square displacement. This study investigates anomalous diffusion processes that exhibit a power-law growth in mean square displacement as time progresses. The first stage is to create the model using random methods, that is, by using random walks. The following statement describes a continuous-time random walk model represented by a series of convolution-type integral equations depicting probability density functions. Fractional differential equations for time and space are derived from the master equations by choosing probability density functions with infinite first and/or second moments. The obtained model equations are analyzed with respect to elementary boundary value problems in constrained fields. The main focus is on studying elementary boundary value problems related to the generalized fractional time diffusion equation, especially using the fractional Caputo derivative. This equation applies a well-established maximum principle to stochastic partial differential equations of the elliptic and parabolic type (SPDEs). This concept is used to make preliminary estimates of the answer before using it to prove the uniqueness of the solution. To prove the existence of a solution, a clearly defined generalized solution is first generated using the spectroscopic method. Under certain additional circumstances, a comprehensive solution may be considered a solution in the traditional sense.
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Lappi, Juha. "Plot size related measurement error bias in tree growth models." Canadian Journal of Forest Research 35, no. 5 (May 1, 2005): 1031–40. http://dx.doi.org/10.1139/x05-019.
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Local tree density around a tree affects tree growth because neighboring trees compete for the same resources. In forestry trees are often sampled by measuring all the trees in sample plots. The total number of the trees in a sample plot or in a larger plot that also encompasses a border zone is often used as the density measurement for all trees in the plot. When the plot density is used as the measurement of local density around a sample tree, the measurement error is correlated both with the measured value and with the true value. Thus none of the standard measurement error assumptions hold. The bias in the estimated density effect is related to the plot size. Assuming random tree locations and a simple linear model including both overall stand density and local density as predictor variables, the bias is analyzed analytically using weighted distributions. The plot size producing the highest coefficient of determination is rather close to the size of the influence zone, but much larger plot sizes are needed for unbiased estimation. It is safe to measure density from a larger plot than that used for sample tree selection. The analysis may give insight for other cases in multilevel modeling where group variables are used to explain individual responses.
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Cavalcante, Diego Helcias, Severino Cavalcante Sousa Júnior, Luciano Pinheiro Silva, Carlos Henrique Mendes Malhado, Raimundo Martins Filho, José Elivalto Guimarães Campêlo, and Karina Rodrigues Santos. "Covariance function of Legendre polynomials for the modeling of Polled Nellore cattle growth in northern Brazil." Semina: Ciências Agrárias 40, no. 2 (April 15, 2019): 781. http://dx.doi.org/10.5433/1679-0359.2019v40n2p781.
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This study aimed to compare random regression models fitted by Legendre orthogonal polynomials and determine which best fits changes in Nellore cattle growth parameters. Age polynomial functions of different orders were evaluated using a random-effect modeling associated with a genetic study of cattle growth curves. For this purpose, weight records (15,148) were performed in Polled Nellore bovines (3,115), aged between 1 and 660 days, reared in northern Brazil and born between 1995 and 2010. The fixed effects of analytical models comprised age-matched groups, heifer calving age (linear and quadratic), and fourth-order Legendre age polynomial (cubic), depicting the mean growth curve. Besides, different order functions were considered for random effects, so that (co) variance associated with genetic effects (direct and maternal) and permanent environmental effects (animal and maternal) could be modeled. Residual variance was fitted by six heterogeneous classes throughout the analyzed period. According to AIC and BIC criteria, the model 6333 allowed the fitting of changes in variance and covariance over time (genetic and environmental). Thus, this model can be used to describe age-related changes in Polled Nellore cattle reared in northern Brazil.
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Tian, Peiru. "Research On Laptop Price Predictive Model Based on Linear Regression, Random Forest and Xgboost." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 265–71. http://dx.doi.org/10.54097/9nx5ad16.
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In today's rapidly evolving technological environment, the portability and versatility of laptops have led to a significant growth in their user base as well, making them essential tools for individuals and businesses alike. Particularly since the COVID-19 pandemic, the rate of Work From Home (WFH) partially or overall WFH has exceeded 30%. Global laptop shipments have also far surpassed desktop PCs so far in 2017. Accurately predicting laptop prices is beneficial for retailers to devise competitive pricing strategies and for consumers to effectively budget and select the most suitable laptops. In this study, we used a dataset of 1320 samples to investigate the significance of features in a laptop price prediction model using linear regression, random forest, and XGBoost methods. We incorporated 13 features in the modeling process, including laptop brand, type, screen size, RAM, GPU, operating system, and weight. Three mathematical models were established to predict the price of laptop. By comparing the regression model metrics RMSE and R2 under linear regression, random forest and XGBoost models, the RMSE under the XGBoost model is 294.11 with an R2 of 0.85. It is evident that the XGBoost model exhibits the smallest RMSE and the highest R2 value closest to 1. This suggests that the XGBoost model provides the highest accuracy and best fit for the predictive model.
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Jensen, Signe M., Muhammad Javaid Akhter, Saiful Azim, and Jesper Rasmussen. "The Predictive Power of Regression Models to Determine Grass Weed Infestations in Cereals Based on Drone Imagery—Statistical and Practical Aspects." Agronomy 11, no. 11 (November 11, 2021): 2277. http://dx.doi.org/10.3390/agronomy11112277.
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Site-specific weed management (SSWM) may reduce herbicide use by identifying weed patches and weed-free areas. However, one major constraint is robust weed detection algorithms that are able to predict weed infestations outside of the training data. This study investigates the predictive power of regression models trained on drone imagery that are used within fields to predict infestations of annual grass weeds in the late growth stages of cereals. The main objective was to identify the optimum sampling strategy for training regression models based on aerial RGB images. The study showed that training based on sampling from the whole range of weed infestations or the extreme values in the field provided better prediction accuracy than random sampling. Prediction models based on vegetation indices (VIs) offered a useful alternative to a more complex random forest machine-learning algorithm. For binary decision-making, linear regression utilizing weed density information resulted in higher accuracy than a logistic regression approach that only relied on information regarding the presence/absence of weeds. Across six fields, the average balanced accuracy based on linear regression was in the range of 75–83%, with the highest accuracy found when the sampling was from the extreme values in the field, and with the lowest accuracy found for random sampling. For future work on training weed prediction models, choosing training sets covering the entire sample space is recommended in favor of random sampling.
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Lewis, R. M., and S. Brotherstone. "A genetic evaluation of growth in sheep using random regression techniques." Animal Science 74, no. 1 (February 2002): 63–70. http://dx.doi.org/10.1017/s1357729800052218.
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AbstractRepeated measures of live weight in growing animals are used to describe the path by which they travel from birth to maturity. A family of growth functions-the Gompertz is one in particular-has been used successfully to describe this journey with relatively few parameters (most importantly mature size and a rate parameter). However, using these functions to differentiate the genetic merit of individual animals to grow is problematic since the estimates of these parameters are highly correlated and are obtained with varying precision among animals. An alternative is random regression (RR) methodology. It allows environmental effects specific to the time of recording to be accounted for and can accommodate genetic differences in the shape of each animal’s growth curve. At present, though, only linear models (polynomials) can pragmatically be fitted with RR. This may be limiting since a priori beliefs about the appropriate form of a growth function, such as the non-linear Gompertz equation, cannot be accommodated. This paper describes the application of RR techniques to describe growth on a population of Suffolk sheep and compares the genetic evaluation predicted from a RR model with that obtained from a more traditional method based on a Gompertz form.The RR model chosen as providing the best fit (P < 0·01) included additive genetic and permanent environmental (between repeat records of an individual) effects fitted to a fifth order polynomial, and dam effects fitted to a third order polynomial. Measurement error was modelled as six classes. The heritability varied at different points along the growth trajectory (from 0·09 at 15 days to 0·33 at 150 days), suggesting that live weight early in a lamb’s life is a different trait to live weight later in life. There was genetic variation in the growth curves of individual animals, which was accounted for by fitting a RR model. Breeding values obtained by RR and a Gompertz approach were moderately to highly correlated (0·81 at 56 days, 0·91 at 150 days). If breeding value for live weight at 150 days of age were the selection criterion, similar individuals would be chosen with both methodologies. The ‘better’ properties and greater flexibility of the RR approach are discussed.
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Fu, L. Y., W. S. Zeng, S. Z. Tang, R. P. Sharma, and H. K. Li. "Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China." Journal of Forest Science 58, No. 3 (March 27, 2012): 101–15. http://dx.doi.org/10.17221/69/2011-jfs.
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The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.
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Sheikh, A. K., S. M. Zubair, M. Younas, and M. O. Budair. "Statistical aspects of fouling processes." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 215, no. 4 (November 1, 2001): 331–54. http://dx.doi.org/10.1177/095440890121500406.
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Fouling in heat exchangers is traditionally characterized by deterministic (linear or nonlinear) kinetic models of fouling deposition and removal processes. This deterministic approach to fouling does not reflect the real situation of heat exchangers subject to fouling. The observations in a real situation of fouling of heat exchangers, when compared with the results obtained from predictive models, show a large discrepancy. This discrepancy in the fouling literature is normally referred to as uncertainty of the process. In this paper an attempt is made to model this uncertainty by characterizing the fouling as a correlated random process. The deterministic kinetic models (available in the literature) are randomized by treating their parameters as random quantities. Three fouling patterns are characterized by Rf(t) = Bt for the linear process, Rf(t) = mtn for the power law process with a falling rate (0 n ≤ 1) and Rf(t) = Rf∗[1 − exp (—t/τ)] for an asymptotic process, where t > 0 and B, m, Rf∗ and τ are the random process parameters with associated distributions. Fouling causes the performance loss of heat exchangers which can be tolerated up to a certain limit (i.e. critical level of fouling, Rfc), and thus it is of interest to find P[R(t) ≤ Rfc] = P(T > t) where T is the time to reach Rfc. Such distributions are developed in this paper, which are validated against the available data in the literature. It is demonstrated that alpha, modified alpha and Weibull are the most appropriate models to characterize the time to reach a critical level of fouling, if the underlying random fouling growth laws are linear, power law and asymptotic respectively. Knowledge of these distributions and the methods to determine their parameters is useful for devising appropriate maintenance and cleaning schedules in a probabilistic framework.
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SIMIONESCU, Mihaela, and Lucian-Liviu ALBU. "THE IMPACT OF STANDARD VALUE ADDED TAX ON ECONOMIC GROWTH IN CEE-5 COUNTRIES: ECONOMETRIC ANALYSIS AND SIMULATIONS." Technological and Economic Development of Economy 22, no. 6 (November 23, 2016): 850–66. http://dx.doi.org/10.3846/20294913.2016.1244710.
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The value added tax (VAT), as an instrument of fiscal policy, might have an important role on economic growth. This study analyzes the impact of standard VAT rate on economic growth in five Central and Eastern European countries (CEE-5) (Bulgaria, Czech Republic, Hungary, Poland and Romania). Different types of panel data models (random effect model, dynamic panel and panel vector-autoregression) over 1995–2015 indicated a positive influence of VAT rate on economic growth. There is a bilateral Granger causality between economic growth and VAT rate. The Bayesian linear models indicate a positive effect of VAT rate on GDP rate only for Hungary. On short-run, the other countries register lower GDP rates when VAT rates increase. Some simulations of economic growth for 2016 and 2018 were made for each CEE-5 country under different assumptions regarding VAT rate values.
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Zhai, Dongxue, Xuefeng Zhao, Yanfei Bai, and Delin Wu. "Effective Evaluation of Green and High-Quality Development Capabilities of Enterprises Using Machine Learning Combined with Genetic Algorithm Optimization." Systems 10, no. 5 (August 24, 2022): 128. http://dx.doi.org/10.3390/systems10050128.
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Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation.
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Kang, Ruiyun. "Sales Prediction of Big Mart based on Linear Regression, Random Forest, and Gradient Boosting." Advances in Economics, Management and Political Sciences 17, no. 1 (September 13, 2023): 201–8. http://dx.doi.org/10.54254/2754-1169/17/20231094.
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With rapid development of machine learning and data science approaches, many retailers are employing sales prediction to aid in strategy formulation and profit growth. However, it is challenging for some small merchants to access vast volumes of data for analysis. This paper investigates the viability of predict sales in a small-scale retail supermarket, and evaluates the performance of linear regression, random forest, and gradient boosting method in the corresponding limited dataset. The selected metrics for analysis are RMSE, MAE, and R2 score. Experiment results indicate that the linear regression model has a relatively large error and suffers from underfitting. The two decision-tree based models, random forest, and gradient boosting, perform similarly, with gradient boosting model outperforms by a small margin. This study illustrates that it is feasible to perform sales forecasting by machine learning techniques on a small collection of data. It also identifies the issues in this process and suggests potential fixed, giving small retailers a guideline for making effective sales predictions.
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Ankita H. Harkare. "Intelligent Crop Management Optimization using Machine Learning Algorithms: A Linear Analytical Approach." Advances in Nonlinear Variational Inequalities 27, no. 3 (August 23, 2024): 198–210. http://dx.doi.org/10.52783/anvi.v27.1367.
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This research paper explores Utilizing machine learning techniques in practice, to enhance crop management recommendations. It leverages historical and real-time data, encompassing weather conditions, soil characteristics, growth stages, and past information. A rigorous data prepossessing procedure is employed to generate a well-structured 1.31 lacs data set. Various methodologies are utilized to construct predictive. These models' effectiveness is evaluated using common assessment metrics, such as Recall, accuracy, precision, and F1-score. To enhance the reliability and transparency of the recommendations, ensemble methods such as Naive Bayes, Support vector machines, decision trees, gradient boosting, and random forests, Linear Regression, and k-Nearest Neighbors}, along with interpretative techniques, are incorporated. The results provide insights into different algorithms and predict crucial agricultural activities such as planting, irrigation, fertilization, and pest control. Among all the machine learning models considered, Naive Bayes emerges as the best-performing model, achieving perfect scores of 1.0 in accuracy, precision, recall, and F1-scores. The second-best machine learning model is Random Forests, which follows closely behind with Achieving remarkable results reaching 0.99. These prognostic parameters have a big impact on agricultural productivity and sustainability. In order to improve crop management productivity and resource efficiency, this study promotes the use of data-driven decision-making in agriculture.
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Gwilliam, Benjamin. "Modelling temporal change in inventory attributes from a LiDAR-derived inventory for the United Counties of Prescott and Russell, Ontario: A comparison of random forest and linear regression methods." Forestry Chronicle 98, no. 1 (November 2022): 28–35. http://dx.doi.org/10.5558/tfc2022-009.
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This study assessed the feasibility of updating a forest inventory derived from 2014 Light Detection and Ranging (LiDAR) data using ground plot data collected in 2021 to model change in basal area, volume, and average stand height. These attributes were determined for a subset (n=32) of stands from the original 2014 inventory. Both 2nd order polynomial regression and random forest learning methods were used to model annual growth increments for these attributes and results were compared. Except for height, the variance explained using random forest regression was greater than that explained using linear regression. As well, root mean square error was lower using random forest as opposed to linear regression for all three attributes, suggesting random forest produced more accurate results overall. Although the random forest results could not be extrapolated to the landscape with confidence due to limitations associated with that approach. Rather, the quadratic equations from the linear regression models were used to predict 2021 landscape values. The results at the landscape scale were deemed to be reasonable in terms of ecological expectations despite recognized model weaknesses. Increasing sample size to capture a greater diversity of stand types and allow for species-specific modeling would no doubt result in much better predictions.
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Nguyen, Thi Phan Thu, and Thi Quynh Anh Nguyen. "Factors affecting the profitability of listed agricultural companies in the Vietnamese stock market." Multidisciplinary Science Journal 6, no. 7 (January 30, 2024): 2024125. http://dx.doi.org/10.31893/multiscience.2024125.
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This paper aims to investigate the factors affecting the profitability of agricultural companies in Vietnam. The study is conducted using data from 30 listed agricultural companies on the Vietnamese stock market during 2020-2022. This study uses linear regression models including ordinary least squares (OLS), fixed effect (FE) and random effect (RE) models. In addition, the tests are also conducted to select the appropriate model. In this study, the company's profitability is measured by return on assets (ROA). The results of the study show that economic growth has a positive impact on profitability; leverage, company size and inflation have a negative impact on profitability, while revenue growth rate and current ratio do not affect the profitability of the business.