Journal articles: 'Semi-random models'

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Dikta, Gerhard. "Asymptotically efficient estimation under semi-parametric random censorship models." Journal of Multivariate Analysis 124 (February 2014): 10–24. http://dx.doi.org/10.1016/j.jmva.2013.10.002.

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Xia, Lirong. "Semi-random Impossibilities of Condorcet Criterion." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 5867–75. http://dx.doi.org/10.1609/aaai.v37i5.25727.

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The Condorcet criterion (CC) is a classical and well-accepted criterion for voting. Unfortunately, it is incompatible with many other desiderata including participation (PAR), half-way monotonicity (HM), Maskin monotonicity (MM), and strategy-proofness (SP). Such incompatibilities are often known as impossibility theorems, and are proved by worst-case analysis. Previous work has investigated the likelihood for these impossibilities to occur under certain models, which are often criticized of being unrealistic. We strengthen previous work by proving the first set of semi-random impossibilities for voting rules to satisfy CC and the more general, group versions of the four desiderata: for any sufficiently large number of voters n, any size of the group 1

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Ohlssen, D. I., L. D. Sharples, and D. J. Spiegelhalter. "Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons." Statistics in Medicine 26, no. 9 (2007): 2088–112. http://dx.doi.org/10.1002/sim.2666.

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Caraballo, Tomás, Renato Colucci, and Xiaoying Han. "Semi-Kolmogorov models for predation with indirect effects in random environments." Discrete and Continuous Dynamical Systems - Series B 21, no. 7 (August 2016): 2129–43. http://dx.doi.org/10.3934/dcdsb.2016040.

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Abd El-Monsef, Mohamed, Elhoussainy Rady, and Ayat Sobhy. "WEIBULL SEMIPARAMETRIC REGRESSION MODELS UNDER RANDOM CENSORSHIP." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 8 (December 22, 2015): 5577–82. http://dx.doi.org/10.24297/jam.v11i8.1209.

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Semiparametric regression is concerned with the flexible combination of non-linear functional relationships in regression analysis. The main advantage of the semiparametric regression models is that any application benefits from regression analysis can also benefit from the semiparametric regression. In this paper, we derived a consistent estimator of parametric portion and nonparametric portion in Weibull semi-parametric regression models under random censorship.

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Rushton, Philip A., Farid Taheri, and David C. Stredulinsky. "Fatigue Response and Characterization of 350WT Steel Under Semi-Random Loading." Journal of Pressure Vessel Technology 129, no. 3 (March 9, 2006): 525–34. http://dx.doi.org/10.1115/1.2748835.

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Novel data obtained through experimental investigation into the fatigue response of 350WT steel, subjected to semi-random loading, comprised of various combinations of intermittent tensile overloads and compressive underloads are presented. An effective model for predicting the fatigue response is also introduced. For that, the capabilities of some of the currently available models are investigated and then an exponential delay model, being capable of accounting for the effects of not only overload ratio, but also stress ratio and overload/underload ratio is introduced. Since most variable amplitude models are based on a constant amplitude model, efforts were also expended to identify a constant amplitude fatigue crack growth model that would be easy to use, requiring the calibration of few (if any) empirical curve-fitting parameters. The integrity of a selected model is examined and results are presented.

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Soullard, Yann, Martin Saveski, and Thierry Artières. "Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models." Pattern Recognition Letters 37 (February 2014): 161–71. http://dx.doi.org/10.1016/j.patrec.2013.03.028.

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Zhang, Yuanqing, and Dianmei Shen. "Estimation of semi-parametric varying-coefficient spatial panel data models with random-effects." Journal of Statistical Planning and Inference 159 (April 2015): 64–80. http://dx.doi.org/10.1016/j.jspi.2014.11.001.

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Loeys, Tom, Catherine Legrand, Antonio Schettino, and Gilles Pourtois. "Semi-parametric proportional hazards models with crossed random effects for psychometric response times." British Journal of Mathematical and Statistical Psychology 67, no. 2 (August 13, 2013): 304–27. http://dx.doi.org/10.1111/bmsp.12020.

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Kazemi, Iraj, and Reyhaneh Rikhtehgaran. "Topics on Dynamic Panel Data Models with Random Effects Using Semi-Parametric Bayesian Approach." Communications in Statistics - Theory and Methods 43, no. 8 (April 2014): 1630–48. http://dx.doi.org/10.1080/03610926.2012.673672.

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Papageorgiou, Georgios, and John Hinde. "Multivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities." Statistics and Computing 22, no. 1 (November 3, 2010): 79–92. http://dx.doi.org/10.1007/s11222-010-9207-y.

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Wisnalmawati, Wisnalmawati, Agus Sasmito Aribowo, and Yunie Herawati. "Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 2 (December 3, 2022): 78–85. http://dx.doi.org/10.25139/ijair.v4i2.5267.

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Sentiment analysis aims to categorize opinions using an annotated corpus to train the model. However, building a high-quality, fully annotated corpus takes a lot of effort, time, and expense. The semi-supervised learning technique efficiently adds training data automatically from unlabeled data. The labeling process, which requires human expertise and requires time, can be helped by an SSL approach. This study aims to develop an SSL-Model for sentiment analysis and to compare the learning capabilities of Naive Bayes (NB) and Random Forest (RF) in the SSL. Our model attempts to annotate opinion documents in Indonesian. We use an ensemble multi-classifier that works on unigrams, bigrams, and trigrams vectors. Our model test uses a marketplace dataset containing rating comments scrapping from Shopee for smartphone products in the Indonesian Language. The research started with data preparation, vectorization using TF-IDF, feature extraction, modeling using Random Forest (RF) and Naïve Bayes (NB), and evaluation using Accuracy and F1-score. The performance of the NB model outperformed previous research, increasing by 5,5%. The conclusion is that SSL performance highly depends on the number of training data and the compatibility of the features or patterns in the document with machine learning. On our marketplace dataset, better to use Random Forest.

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Hsiao, Cheng, Qi Li, Zhongwen Liang, and Wei Xie. "Panel Data Estimation for Correlated Random Coefficients Models." Econometrics 7, no. 1 (February 1, 2019): 7. http://dx.doi.org/10.3390/econometrics7010007.

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This paper considers methods of estimating a static correlated random coefficient model with panel data. We mainly focus on comparing two approaches of estimating unconditional mean of the coefficients for the correlated random coefficients models, the group mean estimator and the generalized least squares estimator. For the group mean estimator, we show that it achieves Chamberlain (1992) semi-parametric efficiency bound asymptotically. For the generalized least squares estimator, we show that when T is large, a generalized least squares estimator that ignores the correlation between the individual coefficients and regressors is asymptotically equivalent to the group mean estimator. In addition, we give conditions where the standard within estimator of the mean of the coefficients is consistent. Moreover, with additional assumptions on the known correlation pattern, we derive the asymptotic properties of panel least squares estimators. Simulations are used to examine the finite sample performances of different estimators.

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Millard, Salomon M., and Frans H. J. Kanfer. "Mixtures of Semi-Parametric Generalised Linear Models." Symmetry 14, no. 2 (February 18, 2022): 409. http://dx.doi.org/10.3390/sym14020409.

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The mixture of generalised linear models (MGLM) requires knowledge about each mixture component’s specific exponential family (EF) distribution. This assumption is relaxed and a mixture of semi-parametric generalised linear models (MSPGLM) approach is proposed, which allows for unknown distributions of the EF for each mixture component while much of the parametric structure of the traditional MGLM is retained. Such an approach inherently allows for both symmetric and non-symmetric component distributions, frequently leading to non-symmetrical response variable distributions. It is assumed that the random component of each mixture component follows an unknown distribution of the EF. The specific member can either be from the standard class of distributions or from the broader set of admissible distributions of the EF which is accessible through the semi-parametric procedure. Since the inverse link functions of the mixture components are unknown, the MSPGLM estimates each mixture component’s inverse link function using a kernel smoother. The MSPGLM algorithm alternates the estimation of the regression parameters with the estimation of the inverse link functions. The properties of the proposed MSPGLM are illustrated through a simulation study on the separable individual components. The MSPGLM procedure is also applied on two data sets.

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Kariuki, C. M., E. D. Ilatsia, C. B. Wasike, I. S. Kosgey, and A. K. Kahi. "Genetic evaluation of growth of Dorper sheep in semi-arid Kenya using random regression models." Small Ruminant Research 93, no. 2-3 (October 2010): 126–34. http://dx.doi.org/10.1016/j.smallrumres.2010.05.011.

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Avola, Giovanni, Ezio Riggi, Fabio Gresta, Orazio Sortino, and Andrea Onofri. "Random effects models, BLUPs and redundancy analyses for grain legume crops in semi-arid environments." European Journal of Agronomy 93 (February 2018): 18–26. http://dx.doi.org/10.1016/j.eja.2017.11.004.

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Macdonald, A. S. "An Actuarial Survey of Statistical Models for Decrement and Transition Data - I: Multiple State, Poisson and Binomial Models." British Actuarial Journal 2, no. 1 (April 1, 1996): 129–55. http://dx.doi.org/10.1017/s1357321700003366.

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ABSTRACTThis paper surveys some statistical models of survival data. A basic model of a random lifetime is defined, and censoring is introduced. Methods based on observations of small segments of lifetimes are compared. Markov and semi-Markov (multiple state) models are recommended as well-understood and flexible models well suited to actuarial data. A Poisson model is discussed as an approximation to a two state model, while traditional Binomial-type models are shown to be more restricted and less tractable than multiple state models.

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Seigal, Anna, and Guido Montufar. "Mixtures and products in two graphical models." Journal of Algebraic Statistics 9, no. 1 (September 24, 2018): 1–20. http://dx.doi.org/10.18409/jas.v9i1.90.

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We compare two statistical models of three binary random variables. One is a mixture model and the other is a product of mixtures model called a restricted Boltzmann machine. Although the two models we study look different from their parametrizations, we show that they represent the same set of distributions on the interior of the probability simplex, and are equal up to closure. We give a semi-algebraic description of the model in terms of six binomial inequalities and obtain closed form expressions for the maximum likelihood estimates. We briefly discuss extensions to larger models.

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Han, Dongxiao, Lei Liu, Xiaogang Su, Bankole Johnson, and Liuquan Sun. "Variable selection for random effects two-part models." Statistical Methods in Medical Research 28, no. 9 (July 13, 2018): 2697–709. http://dx.doi.org/10.1177/0962280218784712.

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Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed “minimum information criterion” (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided.

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Verros, G., S. Natsiavas, and C. Papadimitriou. "Design Optimization of Quarter-car Models with Passive and Semi-active Suspensions under Random Road Excitation." Journal of Vibration and Control 11, no. 5 (May 2005): 581–606. http://dx.doi.org/10.1177/1077546305052315.

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A methodology is presented for optimizing the suspension damping and stiffness parameters of nonlinear quarter-car models subjected to random road excitation. The investigation starts with car models involving passive damping with constant or dual-rate characteristics. Then, we also examine car models where the damping coefficient of the suspension is selected so that the resulting system approximates the performance of an active suspension system with sky-hook damping. For the models with semi-active or passive dual-rate dampers, the value of the equivalent suspension damping coefficient is a function of the relative velocity of the sprung mass with respect to the wheel subsystem. As a consequence, the resulting equations of motion are strongly nonlinear. For these models, appropriate methodologies are first employed for obtaining the second moment characteristics of motions resulting from roads with a random profile. This information is next utilized in the definition of a vehicle performance index, which is optimized to yield representative numerical results for the most important suspension parameters. Special attention is paid to investigating the effect of road quality as well as on examining effects related to wheel hop. Finally, a critical comparison is performed between the results obtained for vehicles with passive linear or bilinear suspension dampers and those obtained for cars with semi-active shock absorbers.

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Grabski, Franciszek. "The Renewal Process Generated By Return Times of Semi-Markov Process in Reliability Models." Journal of KONBiN 43, no. 1 (October 1, 2017): 365–80. http://dx.doi.org/10.1515/jok-2017-0056.

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Abstract The renewal process generated by the return times of semi-Markov process to a given state is considered in the paper. The return time to a state j and also a first passage time from a given state i to the state j of semi-Markov process are basic concepts that are used to determine this process. The systems of equations for distributions, expectations and secondond moments of these random variables are presented. Theorem concerning the asymptotic distribution of the considered renewal process is presented in this article. Moreover an illustrative example from the reliability theory is presented in the paper.

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Yenilmez, İsmail, Ersin Yılmaz, Yeliz Mert Kantar, and Dursun Aydın. "Comparison of parametric and semi-parametric models with randomly right-censored data by weighted estimators: Two applications in colon cancer and hepatocellular carcinoma datasets." Statistical Methods in Medical Research 31, no. 2 (December 13, 2021): 372–87. http://dx.doi.org/10.1177/09622802211061635.

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In this study, parametric and semi-parametric regression models are examined for random right censorship. The components of the aforementioned regression models are estimated with weights based on Cox and Kaplan–Meier estimates, which are semi-parametric and nonparametric methods used in survival analysis, respectively. The Tobit based on weights obtained from a Cox regression is handled as a parametric model instead of other parametric models requiring distribution assumptions such as exponential, Weibull, and gamma distributions. Also, the semi-parametric smoothing spline and the semi-parametric smoothing kernel estimators based on Kaplan–Meier weights are used. Therefore, estimates are obtained from two models with flexible approaches. To show the flexible shape of the models depending on the weights, Monte Carlo simulations are conducted, and all results are presented and discussed. Two empirical datasets are used to show the performance of the aforementioned estimators. Although three approaches gave similar results to each other, the semi-parametric approach was slightly superior to the parametric approach. The parametric approach method, on the other hand, yields good results in medium and large sample sizes and at a high censorship level. All other findings have been shared and interpreted.

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Boulanger, Patrice, Keith Attenborough, Qin Qin, and Chris M. Linton. "Reflection of sound from random distributions of semi-cylinders on a hard plane: models and data." Journal of Physics D: Applied Physics 38, no. 18 (September 2, 2005): 3480–90. http://dx.doi.org/10.1088/0022-3727/38/18/024.

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Kim, Jiwoong. "Applicability of special quasi-random structure models in thermodynamic calculations using semi-empirical Debye–Grüneisen theory." Journal of Alloys and Compounds 650 (November 2015): 564–71. http://dx.doi.org/10.1016/j.jallcom.2015.08.035.

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Zhang, Yizhun, and Qisheng Yan. "Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection." ISPRS International Journal of Geo-Information 11, no. 7 (July 13, 2022): 398. http://dx.doi.org/10.3390/ijgi11070398.

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Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an example, a model based on a semi-supervised framework using particle swarm optimization to optimize extreme learning machines (SS-PSO-ELM) is proposed. Based on the landslide samples, a semi-supervised learning framework is constructed through Density Peak Clustering (DPC), Frequency Ratio (FR), and Random Forest (RF) models to expand and divide the landslide sample data. The landslide susceptibility was predicted using high-trust sample data as the input variables of the data-driven model. The results show that the area under the curve (AUC) valued at the SS-PSO-ELM model for landslide susceptibility prediction is 0.893 and the root means square error (RMSE) is 0.370, which is better than ELM and PSO-ELM models without the semi-supervised framework. It shows that the SS-PSO-ELM model is more effective in landslide susceptibility. Thus, it provides a new research idea for predicting landslide susceptibility.

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Witte, M. J., and T. A. Newell. "Transmission of Solar Radiation Through a Random Medium of Water and Glass." Journal of Solar Energy Engineering 108, no. 3 (August 1, 1986): 199–205. http://dx.doi.org/10.1115/1.3268093.

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Two models are presented for the prediction of solar radiation transmission through a random medium of glass and water. One model is based on two-dimensional radiation exchange through a rectangular lattice of glass and water. This model is also applicable for materials other than glass and water. The other model is a semi-empirical equation which expresses the transmission as an exponential function. Both models predict transmittances within 5–10 percent of experimental data.

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Et.al, Md Azman Shahadan. "Bayesian Hierarchical Growth Model for Experimental Data on the Effectiveness of an Incentive-Based Weight Reduction Method." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 1036–47. http://dx.doi.org/10.17762/turcomat.v12i3.840.

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The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks. At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors. Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.

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Esquível, Manuel L., Nadezhda P. Krasii, and Gracinda R. Guerreiro. "Open Markov Type Population Models: From Discrete to Continuous Time." Mathematics 9, no. 13 (June 25, 2021): 1496. http://dx.doi.org/10.3390/math9131496.

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We address the problem of finding a natural continuous time Markov type process—in open populations—that best captures the information provided by an open Markov chain in discrete time which is usually the sole possible observation from data. Given the open discrete time Markov chain, we single out two main approaches: In the first one, we consider a calibration procedure of a continuous time Markov process using a transition matrix of a discrete time Markov chain and we show that, when the discrete time transition matrix is embeddable in a continuous time one, the calibration problem has optimal solutions. In the second approach, we consider semi-Markov processes—and open Markov schemes—and we propose a direct extension from the discrete time theory to the continuous time one by using a known structure representation result for semi-Markov processes that decomposes the process as a sum of terms given by the products of the random variables of a discrete time Markov chain by time functions built from an adequate increasing sequence of stopping times.

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Gu, Anhui, and Yangrong Li. "A Combined Criterion for Existence and Continuity of Random Attractors for Stochastic Lattice Dynamical Systems." International Journal of Bifurcation and Chaos 27, no. 02 (February 2017): 1750019. http://dx.doi.org/10.1142/s0218127417500195.

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The paper is devoted to establishing a combination of sufficient criterion for the existence and upper semi-continuity of random attractors for stochastic lattice dynamical systems. By relying on a family of random systems itself, we first set up the abstract result when it is convergent, uniformly absorbing and uniformly random when asymptotically null in the phase space. Then we apply the results to the second-order lattice dynamical system driven by multiplicative white noise. It is indicated that the criterion depending on the dynamical system itself seems more applicable than the existing ones to lattice differential models.

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Dshalalow, Jewgeni H. "On intensities of modulated Cox measures." Journal of Applied Mathematics and Stochastic Analysis 11, no. 3 (January 1, 1998): 411–23. http://dx.doi.org/10.1155/s1048953398000343.

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In this paper we introduce and study functionals of the intensities of random measures modulated by a stochastic process ξ, which occur in applications to stochastic models and telecommunications. Modulation of a random measure by ξ is specified for marked Cox measures. Particular cases of modulation by ξ as semi-Markov and semiregenerative processes enabled us to obtain explicit formulas for the named intensities. Examples in queueing (systems with state dependent parameters, Little's and Campbell's formulas) demonstrate the use of the results.

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Seyhan, Emel, Jonathan P. Stewart, and Robert W. Graves. "Calibration of a Semi-Stochastic Procedure for Simulating High-Frequency Ground Motions." Earthquake Spectra 29, no. 4 (November 2013): 1495–519. http://dx.doi.org/10.1193/122211eqs312m.

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Broadband ground motion simulation procedures typically utilize physics-based modeling at low frequencies, coupled with semi-stochastic procedures at high frequencies. The high-frequency procedure considered here combines deterministic Fourier amplitude spectra (dependent on source, path, and site models) with random phase. Previous work showed that high-frequency intensity measures from this simulation methodology attenuate faster with distance and have lower intra-event dispersion than in empirical equations. We address these issues by increasing crustal damping (Q) to reduce distance attenuation bias and by introducing random site-to-site variations to Fourier amplitudes using a lognormal standard deviation ranging from 0.45 for Mw < 7 to zero for Mw 8. Ground motions simulated with the updated parameterization exhibit significantly reduced distance attenuation bias and revised dispersion terms are more compatible with those from empirical models but remain lower at large distances (e.g., > 100 km).

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Nhu, Viet-Ha, Ataollah Shirzadi, Himan Shahabi, Wei Chen, John J. Clague, Marten Geertsema, Abolfazl Jaafari, et al. "Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran." Forests 11, no. 4 (April 9, 2020): 421. http://dx.doi.org/10.3390/f11040421.

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We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

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Allahbakhshi, Hoda, Lindsey Conrow, Babak Naimi, and Robert Weibel. "Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection." Sensors 20, no. 3 (January 21, 2020): 588. http://dx.doi.org/10.3390/s20030588.

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This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.

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SEOANE, NICOLAS. "MODELLING FREE-RANGE CATTLE MOVEMENTS IN FORESTS USING MULTISTATE RANDOM WALKS." Journal of Biological Systems 23, supp01 (January 2015): S43—S54. http://dx.doi.org/10.1142/s0218339015400045.

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In this study multi-state random walk models were tested as general approach to analyse movement states in animal relocation data. For this purpose, GPS collars were fit to free-range cattle inhabiting Andean forests in Patagonia, Argentina. The results indicate that animal trajectories have re-visitation patterns in restricted areas of few kilometres and that a large part of behaviors occur at a time scale lower than six hours. Simple multi-state random walk models proved to be practical in modelling characteristics such as step and turning angles distribution in addition to emergent properties of animal trajectories such as temporal autocorrelation. Re-visitation patterns and the emergence of home-range, among other memory processes could be marked as possible limitations of these models. This work is the first description of free-range cattle movement inhabiting the andean-Patagonian forests in semi-feral conditions.

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Stefanov, Valeri T. "On some waiting time problems." Journal of Applied Probability 37, no. 3 (September 2000): 756–64. http://dx.doi.org/10.1239/jap/1014842834.

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A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

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Stefanov, Valeri T. "On some waiting time problems." Journal of Applied Probability 37, no. 03 (September 2000): 756–64. http://dx.doi.org/10.1017/s0021900200015977.

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A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

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Bogomolny, Eugene. "Barrier billiard and random matrices." Journal of Physics A: Mathematical and Theoretical 55, no. 2 (December 16, 2021): 024001. http://dx.doi.org/10.1088/1751-8121/ac3da6.

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Abstract The barrier billiard is the simplest example of pseudo-integrable models with interesting and intricate classical and quantum properties. Using the Wiener–Hopf method it is demonstrated that quantum mechanics of a rectangular billiard with a barrier in the centre can be reduced to the investigation of a certain unitary matrix. Under heuristic assumptions this matrix is substituted by a special low-complexity random unitary matrix of independent interest. The main results of the paper are (i) spectral statistics of such billiards is insensitive to the barrier height and (ii) it is well described by the semi-Poisson distributions.

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Ruiz-Aĺvarez, Marcos, Francisco Gomariz-Castillo, and Francisco Alonso-Sarría. "Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method." Water 13, no. 2 (January 18, 2021): 222. http://dx.doi.org/10.3390/w13020222.

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Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

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Nsenge Mpia, Héritier, Moïse Katembo Kasolen, Vingi Mutegheki Baraka, and Nephtali Inipaivudu Baelani. "Stacking Regression-Based Model for Predicting Patient's Length of Stay in a Semi Urban Hospital." International Journal of Research Publication and Reviews 04, no. 02 (2023): 273–85. http://dx.doi.org/10.55248/gengpi.2023.4212.

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In this research, the authors found that statistical analysis is very important preliminary phase in Machine Learning, especially for regression problems. Indeed, when the authors developed the first single models using the same algorithms and the same dataset, they obtained poor performances. After verifying the assumptions of the multiple linear regression, they adjusted the used data and produced efficient models. Moreover, as the objective was to apply the stacking model to predict Patient's Length of Stay in a semi urban hospital, the results showed that the stacking regressor performed better than the seven different models implemented (Random Forest, Extra Trees, Decision Tree, XGBoost, Multilayer perceptron, Light GBM, Support Vector Regressor (SVR)) taken individually. The authors combined Random Forest Regressor, Extra Trees Regressor, Decision Tree Regressor, XGBoost, Light GBM, and SVR to build the stacking model. Using secondary data from four services (Pediatrics, Hospitalization, Gynecology, and Neonatology) of a semi-urban hospital, located in a region of ongoing war in eastern Democratic Republic of Congo (DRC), the study examined the minimum length of stay of a patient in hospital when admitted in one of the four above services. Performances were evaluated using MAE, RMSE, MSE, R-squared and Accuracy. The stacking regression model shifted from 85% of accuracy before statistical analysis phase to 91% after applying statistics and from 0.75 to 0.91 as R-squared

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Shankara Krishna, A., Vishwanath Mane, Subba Rao, and Manu. "Effect of data normalisation in estimating wave overtopping discharge parameter of semicircular breakwater using ANN and Random Forest." IOP Conference Series: Earth and Environmental Science 1149, no. 1 (May 1, 2023): 012001. http://dx.doi.org/10.1088/1755-1315/1149/1/012001.

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Abstract Breakwaters are the structures constructed in the coastal areas to maintain calm inside the port or prevent beach erosion. Semi-circular Breakwater (SCB) is an innovative type of Breakwater made of hollow caisson on a base slab with or without perforations. In this study, the wave overtopping discharge parameter of an SCB is estimated using Artificial Neural Network and Random Forest. The data is collected and used in the current research from an experimental investigation conducted in the Wave Mechanics Laboratory of the Department of Water Resources and Ocean Engineering (WROE), NITK Surathkal. Using this experimental data, the ANN and Random Forest models are developed for the prediction of the wave overtopping discharge parameter of an SCB. The performance of the models is evaluated using different statistical parameters. Data with and without normalisation are used separately to check the effect of normalisation in the prediction of wave overtopping discharge parameter using ANN and Random Forest. From the results, it is found that ANN gives better results when the data is normalised. The performance of Random Forest is independent of the data normalisation.

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De Abreu, Jadson Coelho, Carlos Pedro Boechat Soares, and Helio Garcia Leite. "ASSESSING ALTERNATIVES TO ESTIMATE THE STEM VOLUME OF A SEASONAL SEMI-DECIDUOUS FOREST." FLORESTA 47, no. 4 (December 21, 2017): 375. http://dx.doi.org/10.5380/rf.v47i4.54259.

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AbstractThe objective of this study was to evaluate the use of linear and hybrid linear models, artificial neural networks (ANN) and support vector machine (SVM) in the estimation of the stem volume in a Seasonal Semi-deciduous Forest. Cubing data of 99 sample-trees of 15 species were used for this purpose. After analysis, we verified that the inclusion of the species as random effect did not contribute to increase the accuracy of the estimates in the structure of a hybrid model. Artificial neural networks and support vector machines, including species as input categorical variables, were the best alternatives to estimate the stem volume of trees of the Seasonal Semi-deciduous Forest.Keywords: Stem volume; artificial neural networks; support vector machines; hybrid linear models; uneven-aged forest. ResumoAvaliando alternativas para estimar o volume do fuste de uma Floresta Estacional Semidecidual. O objetivo desse estudo foi avaliar o uso de modelos lineares e lineares mistos, redes neurais artificiais (RNA) e máquina de vetor de suporte (MVS) na estimação dos volumes dos fustes de árvores em uma Floresta Estacional Semidecidual. Dados de cubagem de 99 árvores-amostra de 15 espécies foram utilizados para esta finalidade. Após análises, verificou-se que a inclusão das espécies como efeito aleatório não contribuiu para aumentar a exatidão das estimativas na estrutura de um modelo misto. As redes neurais artificiais e as máquinas de vetores de suporte, incluindo as espécies como variáveis categóricas de entrada, foram as melhores alternativas para estimar o volume dos fustes das árvores da Floresta Estacional Semidecidual.Palavras-chaves: Volume do fuste; redes neurais artificiais; máquinas de vetor de suporte; modelos lineares mistos; floresta inequiânea.

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Sánchez-Vidal, Francisco Javier. "A Cautionary Note on the Use of Accounting Semi-Identity-Based Models †." Journal of Risk and Financial Management 16, no. 9 (August 30, 2023): 389. http://dx.doi.org/10.3390/jrfm16090389.

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This study employs a Monte Carlo simulation to see whether accounting identity problems are present in the Fazzari, Hubbard, and Petersen model (1988). The Monte Carlo simulation generates 50,000 random cash flows, Tobin’s Q, and error term variables, which shape an investment variable that is dependent on them. Cash flows and investments are linked by a partial accounting identity, also known as an accounting semi-identity (ASI). An accounting identity is, for example, an equality between the left and right sides of a balance sheet. An ASI is not a complete one since one or more components of the accounting identity are missing. The estimated coefficients of an ASI do not represent reality, according to the OLS estimations. The regression tells us less about causality the closer the data are to the accounting identity. This is the first time that the biases of OLS estimations in an ASI-based model have been demonstrated.

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Bohlouli, M., H. Mohammadi, and S. Alijani. "Genetic evaluation and genetic trend of growth traits of Zandi sheep in semi-arid Iran using random regression models." Small Ruminant Research 114, no. 2-3 (September 2013): 195–201. http://dx.doi.org/10.1016/j.smallrumres.2013.07.005.

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Cheng, Wen, Gurdiljot Singh Gill, Tom Vo, Jiao Zhou, and Taha Sakrani. "Use of Bivariate Dirichlet Process Mixture Spatial Model to Estimate Active Transportation-Related Crash Counts." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (July 1, 2018): 105–15. http://dx.doi.org/10.1177/0361198118782797.

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The current paper presents the comprehensive analysis of a bivariate Dirichlet process mixture spatial model for estimation of pedestrian and bicycle crash counts. This study focuses on active transportation at traffic analysis zone (TAZ) level by developing a semi-parametric model that accounts for the unobserved heterogeneity by combining the strengths of bivariate specification for correlation among crash modes; spatial random effects for the impact of neighboring TAZs; and Dirichlet process mixture for random intercept. Three alternate models, one Dirichlet and two parametric, are also developed for comparison based on different criteria. Bicycle and pedestrian crashes are observed to share three influential variables: the positive correlation of K12 student enrollment; the bike-lane density; and the percentage of arterial roads. The heterogeneity error term demonstrates the presence of statistically significant correlation among the bicycle and pedestrian crashes, whereas the spatial random effect term indicates the absence of a significant correlation for the area under focus. The Dirichlet models are consistently superior to non-Dirichlet ones under all evaluation criteria. Moreover, the Dirichlet models exhibit the capability to identify latent distinct subpopulations and suggest that the normal assumption of intercept associated with traditional parametric models does not hold true for the TAZ-level crash dataset of the current study.

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Nakhost, Hootan, and Martin Mueller. "A Theoretical Framework for Studying Random Walk Planning." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 57–64. http://dx.doi.org/10.1609/socs.v3i1.18247.

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Random walks are a relatively new component used in several state of the art satisficing planners. Empirical results have been mixed: while the approach clearly outperforms more systematic search methods such as weighted A* on many planning domains, it fails in many others. So far, the explanations for these empirical results have been somewhat ad hoc. This paper proposes a formal framework for comparing the performance of random walk and systematic search methods. Fair homogenous graphs are proposed as a graph class that represents characteristics of the state space of prototypical planning domains, and is simple enough to allow a theoretical analysis of the performance of both random walk and systematic search algorithms. This gives well-founded insights into the relative strength and weaknesses of these approaches. The close relation of the models to some well-known planning domains is shown through simplified but semi-realistic planning domains that fulfill the constraints of the models. One main result is that in contrast to systematic search methods, for which the branching factor plays a decisive role, the performance of random walk methods is determined to a large degree by the Regress Factor, the ratio between the probabilities of progressing towards and regressing away from a goal with an action. The performance of random walk and systematic search methods can be compared by considering both branching and regress factors of a state space.

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Zhou, Chenyan, Yahong Zhang, Ling Chen, and Zhongxin Wu. "Fatigue strength analysis of semi-submersible platforms based on wide-band stress." Journal of Physics: Conference Series 2553, no. 1 (August 1, 2023): 012005. http://dx.doi.org/10.1088/1742-6596/2553/1/012005.

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Abstract As the focus of ocean resource exploitation shifts from shallow sea to ultra-deep sea, semi-submersible platforms are far away from the coastline for a long time. The semi-submersible platform structure is more prone to fatigue damage under complex sea conditions. The stress range and peaks are considered in the conventional frequency domain spectral method to obey the narrow-band distribution, which makes the results of fatigue assessment tend to be conservative. To solve the problems above, a wide-band fatigue assessment method based on Rice distribution is proposed from the perspective of stress distribution. Different methods are used to solve the error functions involved in fatigue calculations to obtain different approximate equivalent stress models. Finally, the broadband fatigue analysis of a semi-submersible platform in the South China Sea was carried out under different models, and the results showed that: The error of the wide-band fatigue calculation based on Rice distribution is related to the selected equivalent stress model, and a suitable model needs to be selected according to the bandwidth coefficient of the structure. The results obtained provide a reference for the safety assessment of pipelines in service. The research provides a reference for the fatigue assessment of semi-submersible platforms under random broadband loads.

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Gupta, Ashish, Nilanjan Bharadwaj, and Vikas Rastogi. "Computational Framework of Various Semi-Active Control Strategies for Road Vehicles Thorough Bondgraphs." International Journal of System Dynamics Applications 10, no. 4 (October 2021): 1–29. http://dx.doi.org/10.4018/ijsda.20211001.oa9.

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Vehicle suspension system plays a vital role in diminishing the vibration caused by the road roughness and prevent it from transmitting to the driver and the passenger. The semi-active suspensions contain spring and damping elements with variable properties, which can be changed by an external control. The work presented here is concerned with semi-active damper control for vibration isolation of base disturbances. Numerous control algorithms for semi active system had been suggested in the past, performed experimentally and validated with various computational models.In this work, the 2-DOF quarter car model with semi-active suspension, controlled by skyhook and balance logic with on-off and continuous control algorithms is being studied.The computational models are subjected to various road profiles like single half sine bump, random road disturbanceas typical Indian road scenario. So that the performance can be done as real time inputs. The simulation is being carried out on Matlab or Simulink.

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Zhang, Min, Guohua Geng, and Jing Chen. "Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations." Entropy 22, no. 2 (February 22, 2020): 252. http://dx.doi.org/10.3390/e22020252.

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Increasingly, popular online museums have significantly changed the way people acquire cultural knowledge. These online museums have been generating abundant amounts of cultural relics data. In recent years, researchers have used deep learning models that can automatically extract complex features and have rich representation capabilities to implement named-entity recognition (NER). However, the lack of labeled data in the field of cultural relics makes it difficult for deep learning models that rely on labeled data to achieve excellent performance. To address this problem, this paper proposes a semi-supervised deep learning model named SCRNER (Semi-supervised model for Cultural Relics’ Named Entity Recognition) that utilizes the bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) model trained by seldom labeled data and abundant unlabeled data to attain an effective performance. To satisfy the semi-supervised sample selection, we propose a repeat-labeled (relabeled) strategy to select samples of high confidence to enlarge the training set iteratively. In addition, we use embeddings from language model (ELMo) representations to dynamically acquire word representations as the input of the model to solve the problem of the blurred boundaries of cultural objects and Chinese characteristics of texts in the field of cultural relics. Experimental results demonstrate that our proposed model, trained on limited labeled data, achieves an effective performance in the task of named entity recognition of cultural relics.

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Oh, Seungmin, Akm Ashiquzzaman, Dongsu Lee, Yeonggwang Kim, and Jinsul Kim. "Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning." Sensors 21, no. 8 (April 14, 2021): 2760. http://dx.doi.org/10.3390/s21082760.

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In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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Goydaragh, Maryam Ghebleh, Ali Asghar Jafarzadeh, Farzin Shahbazi, Shahin Oustan, Ruhollah Taghizadeh-Mehrjardi, and Marcos Lado. "Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models." Revista Brasileira de Engenharia Agrícola e Ambiental 23, no. 6 (June 2019): 460–66. http://dx.doi.org/10.1590/1807-1929/agriambi.v23n6p460-466.

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ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran.

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Journal articles on the topic 'Semi-random models'

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