Relevant bibliographies by topics / Multi variate regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi variate regression'

Author: Grafiati
Published: 8 September 2023

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Journal articles on the topic "Multi variate regression"

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Luo, Chongliang, Jin Liu, Dipak K. Dey, and Kun Chen. "Canonical variate regression." Biostatistics 17, no. 3 (February 9, 2016): 468–83. http://dx.doi.org/10.1093/biostatistics/kxw001.

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Abstract In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an $F_2$ intercross mice study and an alcohol dependence study.
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Chen, Zexun, Bo Wang, and Alexander N. Gorban. "Multivariate Gaussian and Student-t process regression for multi-output prediction." Neural Computing and Applications 32, no. 8 (December 31, 2019): 3005–28. http://dx.doi.org/10.1007/s00521-019-04687-8.

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AbstractGaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student-t distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real-data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.
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Åström, Oskar, Henrik Hedlund, and Alexandros Sopasakis. "Machine-Learning Approach to Non-Destructive Biomass and Relative Growth Rate Estimation in Aeroponic Cultivation." Agriculture 13, no. 4 (March 30, 2023): 801. http://dx.doi.org/10.3390/agriculture13040801.

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We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate based onfrom a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5-day period. The results show that images taken from a top-down perspective produce better results for the multi-variate regression network, while images taken from the side are better for the ResNet-50 neural network. In addition, using images from both cameras improves the biomass estimates from the ResNet-50 network, but not those from the multivariatemulti-variatemultivariate regression. However, all relative growth rate estimates were improved by using images from both cameras. We found that the best biomass estimates are produced from the multi-variate regression model trained on top camera images using a moving average filter resulting in a root mean square error of 0.0466 g. The best relative growth rate estimates were produced from the ResNet-50 network training on images from both cameras resulting in a root mean square error of 0.1767 g/(g·day).
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de Laat, A. T. J., R. J. van der A, and M. van Weele. "Tracing the second stage of Antarctic ozone hole recovery with a "big data" approach to multi-variate regressions." Atmospheric Chemistry and Physics Discussions 14, no. 12 (July 14, 2014): 18591–640. http://dx.doi.org/10.5194/acpd-14-18591-2014.

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Abstract. This study presents a sensitivity analysis of multi-variate regressions of recent springtime Antarctic vortex ozone trends using a "big data" ensemble approach. Multi-variate regression methods are widely used for studying the variability and detection of ozone trends. Based on multi-variate regression analysis of total Antarctic springtime vortex ozone it has been suggested that the observed increase of ozone since the late 1990s is statistically significant and can be attributed to decreasing stratospheric halogens (Salby et al., 2011, 2012; Kuttippurath et al., 2013). We find that, when considering uncertainties that have not been addressed in these studies, this conclusion on ozone recovery is not warranted. An ensemble of regressions is constructed based on the analysis of uncertainties in the applied ozone record as well as of uncertainties in the various applied regressors. The presented combination of ensemble members spans up the uncertainty range with about 35 million different regressions. The poleward heat flux (Eliassen–Palm Flux) and the effective chlorine loading explain, respectively, most of the short-term and long-term variability in different Antarctic springtime total ozone records. The inclusion in the regression of stratospheric volcanic aerosols, solar variability, the Quasi-Biennial Oscillation and the Southern Annular Mode is shown to increase rather than to decrease the overall uncertainty in the attribution of Antarctic springtime ozone because of large uncertainties in their respective records. Calculating the trend significance for the ozone record from the late 1990s onwards solely based on the fit of the effective chlorine loading should be avoided, as this does not take fit residuals into account and thereby results in too narrow uncertainty intervals. When taking fit residuals into account, we find that less than 30% of the regressions in the full ensemble result in a statistically significant positive springtime ozone trend over Antarctica from the late 1990s to either 2010 or 2012. Analysis of choices and uncertainties in time series show that, depending on choices in time series and parameters, the fraction of statistically significant trends in parts of the ensemble can range from negligible to more than 90%. However, we were unable to detect a robust statistically significant positive trend in Antarctic springtime vortex ozone in the ensemble.
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Gholizadeh, Pouya, and Behzad Esmaeili. "Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors." International Journal of Environmental Research and Public Health 17, no. 13 (July 6, 2020): 4852. http://dx.doi.org/10.3390/ijerph17134852.

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The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.
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Hongchao, Ma, and Li Deren. "Enhancing group resolution of TM6 based on multi-variate regression model and semi-variogram function." Geo-spatial Information Science 4, no. 1 (January 2001): 43–49. http://dx.doi.org/10.1007/bf02826636.

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Merz, B., H. Kreibich, and U. Lall. "Multi-variate flood damage assessment: a tree-based data-mining approach." Natural Hazards and Earth System Sciences 13, no. 1 (January 11, 2013): 53–64. http://dx.doi.org/10.5194/nhess-13-53-2013.

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Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damage-influencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.
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Benson, Roger B. J., and Philip D. Mannion. "Multi-variate models are essential for understanding vertebrate diversification in deep time." Biology Letters 8, no. 1 (June 22, 2011): 127–30. http://dx.doi.org/10.1098/rsbl.2011.0460.

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Statistical models are helping palaeontologists to elucidate the history of biodiversity. Sampling standardization has been extensively applied to remedy the effects of uneven sampling in large datasets of fossil invertebrates. However, many vertebrate datasets are smaller, and the issue of uneven sampling has commonly been ignored, or approached using pairwise comparisons with a numerical proxy for sampling effort. Although most authors find a strong correlation between palaeodiversity and sampling proxies, weak correlation is recorded in some datasets. This has led several authors to conclude that uneven sampling does not influence our view of vertebrate macroevolution. We demonstrate that multi-variate regression models incorporating a model of underlying biological diversification, as well as a sampling proxy, fit observed sauropodomorph dinosaur palaeodiversity best. This bivariate model is a better fit than separate univariate models, and illustrates that observed palaeodiversity is a composite pattern, representing a biological signal overprinted by variation in sampling effort. Multi-variate models and other approaches that consider sampling as an essential component of palaeodiversity are central to gaining a more complete understanding of deep time vertebrate diversification.
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Amos, Kanyesiga Johnson, and Bazinzi Natamba. "The Impact of Training and Development on Job Performance in Ugandan Banking Sector." Journal on Innovation and Sustainability. RISUS ISSN 2179-3565 6, no. 2 (August 10, 2015): 65. http://dx.doi.org/10.24212/2179-3565.2015v6i2p65-71.

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The study examined the impact of training and development on job performance in the Banking sector in Uganda among the selected four banks of Equity Bank, Bank of Africa, Barclays Bank Uganda and Centenary Bank and specifically looked at the relationship between training needs identification, training methods, monitoring, evaluation of training and job performance in the banking sector in Uganda. The study used correlation research design to address the relationship between variables. The study involved managers, heads of departments at each bank and employees. Data was collected using questionnaires to facilitate quantitative approaches in the study. Data was analyzed at three levels that is; univerariate, bi-variate and multi-variate. Univeriate analysis fetched descriptive statistics in form frequencies and percentages while bivariate analysis obtained correlations between variables. At multivariate level a logistic regression model was used to ascertain the magnitude of effect of each independent variable on the dependent variable. Study findings at a bi-variate level revealed a positive and significant relationship between the independent variables (identify training needs, identify training objectives, training content, on the job training technique, off the job training technique, skills application and Knowledge application) and the dependent variable (job performance). At the multi-variate level, it was revealed that all independent variables except knowledge application in the training and evaluation process explain 69% of job performance in the model. It was concluded that identification of training objectives, identification of training objectives and skills application have a positive significant effect on job performance in the banking sector in Uganda. It was therefore recommended that there is need to need to streamline the needs assessment process before the training process, endeavor to clearly define training objectives and have a strict monitoring and evaluation process on trainees.
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Barnes, R. J., M. S. Dhanoa, and Susan J. Lister. "Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra." Applied Spectroscopy 43, no. 5 (July 1989): 772–77. http://dx.doi.org/10.1366/0003702894202201.

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Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.
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Dissertations / Theses on the topic "Multi variate regression"

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Foxall, Robert John. "Likelihood analysis of the multi-layer perceptron and related latent variable models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327211.

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Wu, Hao. "Probabilistic Modeling of Multi-relational and Multivariate Discrete Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74959.

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Modeling and discovering knowledge from multi-relational and multivariate discrete data is a crucial task that arises in many research and application domains, e.g. text mining, intelligence analysis, epidemiology, social science, etc. In this dissertation, we study and address three problems involving the modeling of multi-relational discrete data and multivariate multi-response count data, viz. (1) discovering surprising patterns from multi-relational data, (2) constructing a generative model for multivariate categorical data, and (3) simultaneously modeling multivariate multi-response count data and estimating covariance structures between multiple responses. To discover surprising multi-relational patterns, we first study the ``where do I start?'' problem originating from intelligence analysis. By studying nine methods with origins in association analysis, graph metrics, and probabilistic modeling, we identify several classes of algorithmic strategies that can supply starting points to analysts, and thus help to discover interesting multi-relational patterns from datasets. To actually mine for interesting multi-relational patterns, we represent the multi-relational patterns as dense and well-connected chains of biclusters over multiple relations, and model the discrete data by the maximum entropy principle, such that in a statistically well-founded way we can gauge the surprisingness of a discovered bicluster chain with respect to what we already know. We design an algorithm for approximating the most informative multi-relational patterns, and provide strategies to incrementally organize discovered patterns into the background model. We illustrate how our method is adept at discovering the hidden plot in multiple synthetic and real-world intelligence analysis datasets. Our approach naturally generalizes traditional attribute-based maximum entropy models for single relations, and further supports iterative, human-in-the-loop, knowledge discovery. To build a generative model for multivariate categorical data, we apply the maximum entropy principle to propose a categorical maximum entropy model such that in a statistically well-founded way we can optimally use given prior information about the data, and are unbiased otherwise. Generally, inferring the maximum entropy model could be infeasible in practice. Here, we leverage the structure of the categorical data space to design an efficient model inference algorithm to estimate the categorical maximum entropy model, and we demonstrate how the proposed model is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and US census datasets, and demonstrate its feasibility using an epidemic simulation application. Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses. Existing methods for univariate count responses cannot be easily extended to the multivariate case since the dependency among multiple responses needs to be properly accounted for. To model multivariate data with multiple count responses, we propose a novel multivariate Poisson log-normal model (MVPLN). By simultaneously estimating the regression coefficients and inverse covariance matrix over the latent variables with an efficient Monte Carlo EM algorithm, the proposed model takes advantages of association among multiple count responses to improve the model prediction accuracy. Simulation studies and applications to real world data are conducted to systematically evaluate the performance of the proposed method in comparison with conventional methods.
Ph. D.
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Galindo-Prieto, Beatriz. "Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130579.

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Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV). The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.
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Senapati, Swagatika. "Modelling of geotechnical structures using multi-variate adaptive regression spline (MARS) and genetic programming (GP)." Thesis, 2013. http://ethesis.nitrkl.ac.in/5345/1/211CE1017.pdf.

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The soil is considered as a complex material produced by the weathering of solid rock. Due to its uncertain behavior, modeling the behavior of such materials is complex by using more traditional forms of mechanistic based engineering methods like analytical and finite element methods etc. Very often it is difficult to develop theoretical/statistical models due to the complex nature of the problem and uncertainty in soil parameters. These are situations where data driven approach has been found to more appropriate than model oriented approach. To take care of such problems in artificial intelligence (AI) techniques has been developed in the computational methods. Though AI techniques has proved to have the superior predictive ability than other traditional methods for modeling complex behavior of geotechnical engineering materials, still it is facing some criticism due to the lack of transparency, knowledge extraction and model uncertainty. To overcome this problem there are developments of improvised AI techniques. Different AI techniques as ‘black box’ i.e artificial neural network (ANN), ‘grey box’ i.e Genetic programming (GP) and ‘white box’ i.e multivariate adaptive regression spline (MARS) depending upon its transparency and knowledge extraction. Here, in this study of GP and MARS ‘grey box’ and ‘white box’ AI techniques are applied to some geotechnical problems such as prediction of lateral load capacity of piles in clay, pull-out capacity of ground anchor, factor of safety of slope stability analysis and ultimate bearing capacity of shallow foundations.. Different statistical criteria are used to compare the developed GP and MARS models with other AI models like ANN and support vector machine (SVM) models. It was observed that for the problems considered in the present study, the MARS and GP model are found to be more efficient than ANN and SVM model and the model equations are also found to be more comprehensive.
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Steyn, Hendrik Stefanus. "The use of effect sizes in credit rating models." Diss., 2014. http://hdl.handle.net/10500/18790.

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The aim of this thesis was to investigate the use of effect sizes to report the results of statistical credit rating models in a more practical way. Rating systems in the form of statistical probability models like logistic regression models are used to forecast the behaviour of clients and guide business in rating clients as “high” or “low” risk borrowers. Therefore, model results were reported in terms of statistical significance as well as business language (practical significance), which business experts can understand and interpret. In this thesis, statistical results were expressed as effect sizes like Cohen‟s d that puts the results into standardised and measurable units, which can be reported practically. These effect sizes indicated strength of correlations between variables, contribution of variables to the odds of defaulting, the overall goodness-of-fit of the models and the models‟ discriminating ability between high and low risk customers.
Statistics
M. Sc. (Statistics)
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Books on the topic "Multi variate regression"

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Halperin, Sandra, and Oliver Heath. 17. A Guide to Multivariate Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0017.

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This chapter extends the principles of bivariate analysis to multivariate analysis, which takes into account more than one independent variable and the dependent variable. With multivariate analysis, it is possible to investigate the impact of multiple factors on a dependent variable of interest, and to compare the explanatory power of rival hypotheses. Multivariate analysis can also be used to develop and test multi-causal explanations of political phenomena. After providing an overview of the principles of multivariate analysis, and the different types of analytical question to which they can be applied, the chapter shows how multivariate analysis is carried out for statistical control purposes. More specifically, it explains the use of OLS regression and logistic regression, the latter of which builds on cross-tabulation, to carry out multivariate analysis. It also discusses the use of multivariate analysis to debunk spurious relationships and to illustrate indirect causality.
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Book chapters on the topic "Multi variate regression"

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Bonamente, Massimiliano. "Multi-variable Regression." In Statistics and Analysis of Scientific Data, 247–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0365-6_13.

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Bonamente, Massimiliano. "Multi-Variable Regression." In Statistics and Analysis of Scientific Data, 165–75. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6572-4_9.

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Chen, Wan-Ping, Ying Nian Wu, and Ray-Bin Chen. "Bayesian Variable Selection for Multi-response Linear Regression." In Technologies and Applications of Artificial Intelligence, 74–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13987-6_8.

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Yang, Kaifeng, and Michael Affenzeller. "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression." In Lecture Notes in Computer Science, 176–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_13.

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AbstractSurrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
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Olaru, Gabriel, Alexander Robitzsch, Andrea Hildebrandt, and Ulrich Schroeders. "An Illustration of Local Structural Equation Modeling for Longitudinal Data: Examining Differences in Competence Development in Secondary Schools." In Methodology of Educational Measurement and Assessment, 153–76. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27007-9_7.

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AbstractIn this chapter, we discuss how a combination of longitudinal modeling and local structural equation modeling (LSEM) can be used to study how students’ context influence their growth in educational achievement. LSEM is a nonparametric approach that allows for the moderation of a structural equation model over a continuous variable (e.g., socio-economic status; cultural identity; age). Thus, it does not require the categorization of continuous moderators as applied in multi-group approaches. In contrast to regression-based approaches, it does not impose a particular functional form (e.g., linear) on the mean-level differences and can spot differences in the variance-covariance structure. LSEM can be used to detect nonlinear moderation effects, to examine sources of measurement invariance violations, and to study moderation effects on all parameters in the model. We showcase how LSEM can be implemented with longitudinal of the National Educational Panel Study (NEPS) using the R-package sirt. In more detail, we examine the effect of parental education on math and reading competence in secondary school across three measurement occasions, comparing LSEM to regression based approaches and multi-group confirmatory factor analysis. Results provide further evidence of the strong influence of the educational background of the family. This chapter offers a new approach to study inter-individual differences in educational development.
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Xu, You. "A Gradient-Based ELM Algorithm in Regressing Multi-variable Functions." In Advances in Neural Networks - ISNN 2006, 653–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_96.

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Seo, In-Yong, Bok-Nam Ha, and Min-Ho Park. "Multi-response Variable Optimization in Sensor Drift Monitoring System Using Support Vector Regression." In Intelligent Information and Database Systems, 21–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20042-7_3.

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Barba, Lida, Nibaldo Rodríguez, Ana Congacha, and Lady Espinoza. "Multi-resolution SVD, Linear Regression, and Extreme Learning Machine for Traffic Accidents Forecasting with Climatic Variable." In Lecture Notes in Networks and Systems, 501–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82196-8_37.

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Do Luu, Duc, and Luu Minh Hai. "Multi-variable Regressive Models for Diagnostics of the Unbalances on Rapid Rotor in Shop Dynamic Balance." In Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020), 267–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69610-8_37.

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Oda, Ryoya. "Kick-One-Out-Based Variable Selection Method Using Ridge-Type $$C_{p}$$ Criterion in High-Dimensional Multi-response Linear Regression Models." In Intelligent Decision Technologies, 193–202. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2969-6_17.

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Conference papers on the topic "Multi variate regression"

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Uddin, Rokan, Fahim Irfan Alam, Avisheak Das, and Sadia Sharmin. "Multi-Variate Regression Analysis for Stock Market price prediction using Stacked LSTM." In 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE, 2022. http://dx.doi.org/10.1109/iciset54810.2022.9775911.

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Sastry, Rambhatla G., and Sumedha Chahr. "MULTI-VARIATE REGRESSION ANALYSIS OF GEO-ELECTRIC IMAGING AND GEOTECHNICAL SITE INVESTIGATION TEST RESULTS - A CASE STUDY." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2015. Society of Exploration Geophysicists and Environment and Engineering Geophysical Society, 2016. http://dx.doi.org/10.4133/sageep.29-033.

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Sharma, Akash, and Brandon Guttery. "Multivariate Modeling of Terminal Decline Rate in Parent and Child Wells in Unconventional Reservoirs." In SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200876-ms.

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Abstract As operators shift their focus toward operating within cashflow, understanding the true potential of these unconventional resources is becoming increasingly important. Simultaneously, accurate modeling of EURs in shale wells is becoming increasingly complicated. There are multiple factors at play for this increase in complexity, key amongst them, are well interactions. Well interactions or interference have increased with the concentration of field development in core areas of various basins and have completely changed with production behavior in shale wells. The present paper handles this multi-variable problem by incorporating well design, completion and petrophysical variables in a prediction model. Furthermore, the analysis is presented from a viewpoint of parent, child, parent/child and co-completed wells to accurately understand the variability in the driving factors. Terminal decline rate in shale wells is the decline rate wells settle at once the pressure transient reaches the boundary of the well. At this point, the well transitions to a boundary dominated flow regime and continues to drain from a fixed area. Estimating the rate of terminal decline is critical in accurate EUR modeling because changes in transition point can have a significant impact on production behavior of the well and in-turn EUR. The present paper attempts to predict the transition point using an ACE Non-Linear Regression model which is trained on a large multi-variate dataset. Variables incorporated in this analysis include terminal decline month, gas-oil-ratio based of the first three months of production, horizontal length, oil EUR, proppant per foot, average distance from the base of the producing zone, nearest neighbor mean spacing, and hydrocarbon in-place. In order to determine spacing status and nearest wellbore distances, a segment-wise analytical distance approach was taken. These distances and spacing status flags were incorporated into a multi-variate model in-order to model terminal decline rates. The transformations observed from the model showed high dependence on terminal decline month and oil EUR. However, this was less pronounced in parent/child and child wells. In parent/child and child wells completion metrics and HCIP more significantly influenced production behavior. Specifically, child wells saw a higher dependence on first three-month GOR and lateral length compared to parent/child wells which had a higher dependence on proppant per foot and average distance from the base of the producing formation. Additionally, spacing showed a moderate impact on transition point and associated terminal decline rates, but overall increased spacing caused a delayed transition point and consequently a lower terminal decline rate. Understanding how cause-and-effect relationships between parent and child wells differ offers a unique perspective into production behavior and consequently provides better insights into infill wells placement and production prediction. The present paper offers a unique perspective in looking at a key decline variable, transition point, for shale reservoirs. By using multivariate analysis, it incorporates the incremental complexity of the modeling effort and attempts to provide best practices in understanding the impact on production behavior. Furthermore, by incorporating a segment-wise analytical distance approach to determine spacing, the paper adds to the existing body of literature by providing a new perspective for a well interaction standpoint and defines the cause and effect relationships within.
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Roy Chowdhury, Joydeb, Aditya Chatterjee, Saikat Basu, Sayan Goswami, Suvradipta Saha, Surajit Poddar, and Anup Bhattacharjee. "Design Methodology of Solar Powered Load Controller Using Sensor Assisted Remote Battery State of Charge Estimator." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37008.

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One of the main problems of a solar PV plant is low generation of electricity during bad weather condition when the generated power is less than the claimed demand of power. Under this condition it is not possible to generate more electricity as per demand once the power plant is designed. When the supply of electricity to the consumers reduces drastically and when there is no option for manipulation of power — a blackout out or load shedding is the inevitable. Online demand regulation i.e. regulating the claimed demand of the individual consumer depending on predicted power generation is an alternate option in this constrained situation. The present paper will address this particular problem through probabilistic duration estimation power against a critical load using statistical model. Our goal is to design a predictive model considering the environmental fluctuation of solar clarity index into consideration and incorporates a policy of meeting the critical base load primarily and loads exceeding the base load secondarily depending on predicted energy generation. A modified model of MARS (Multi variate adaptive regression spline) is developed and finally used for prediction of battery state of health from a remote end using sunlight intensity (similar to clarity index) using remote sensor technology. We use Multivariate adaptive smoothing spline with adaptive smoothing features of noisy data available from light sensor.
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Slaveski, Trajko, and Darko Lazarov. "How Do Institutions Determine Economic Growth? Evidеnce from Central and Eastern Europe before and during Global Economic Crisis." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01040.

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We investigate the influence of institutions on economic growth and the level of income per capita in CEE region, before and during the global economic crisis. We use principal factor component analysis in order to create a more reliable and representative variable that will measure the institutional quality in our regression models, and avoid the multi colinearity, a common statistical weakness for this type of regression models. The results from panel (random and fixed effects) regressions and GMM dynamic panel regression lead to two contrasting insights. The first regression model shows positive and statistically significant correlation between institutions and economic growth, which would imply that the CEE countries that have created a strong institutional capacity during transition and post-transition period have experienced higher economic growth. The second regression model, which refers to the global economic crisis period, shows a negative influence of institutions on economic growth for the same sample of countries. One explanation for this result might be the fact that countries with a higher degree of integration into the EU were also more vulnerable to the global economic crisis.
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Oliveira, Emerson V., David H. do Santos, and Luiz M. G. Goncalves. "Auto-regressive Multi-variable Auto-encoder." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sibgrapi.est.2022.23279.

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Due to the global pandemic disclaimer caused by the SARS-COV-2 virus propagation, also called COVID-19, governments, institutions, and researchers have mobilized intending to try to mitigate the effects caused by the virus on society. Some approaches were proposed and applied to try to make predictions of the behavior of possible pandemics indicators. Among those methodologies, some models are data orientated, also known as data-driven, which had considerable prominence over the others. Artificial Neural Networks are a widely used model among datadriven models. In this work, we propose a novel Auto-Encoder RNA architecture. This architecture aims to forecast time series related to the COVID-19 pandemic, particularly the number of deaths. The model uses as inputs possible associated time series with the desired forecasting. In the experiments, we used the representation in time series from the number of COVID-19 cases, deaths, temperature, humidity, and the Air Quality Index (AQI) of São Paulo city in Brazil. The results show that the model has a prominent forecasting accuracy for the COVID-19 deaths time series.
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Omion, Osemanre Ossy, Chioma Maduewesi, and Emeke Chukwu. "A Novel Approach to Predicting Combustion Emission Using Ambient Air Quality Parameters in Onshore Eastern Nigeria." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207139-ms.

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Abstract The paper aims to estimate the tCO2e from flare stack sites in the Eastern zone of Nigeria and review air monitoring done at the flare sites with the objective of establishing a model for gas tCO2e emission and gaseous pollutants. It focuses on the South-Eastern region of Nigeria where oil and gas production are being carried out (Imo and Abia states). It zero-in on the hydrocarbon processing and handling facilities (flowstation) and the gas flared volumes. The study was carried out using representative data from an onshore flowstation in Eastern Nigeria. The data consist of gas flared volumes from year 2013-2017 and ambient gaseous emission from air quality report done on the same location. Univariate regression and correlation using Excel were carried out on yearly average ambient air quality parameters (VOC, NOx, CO, SOx, CH4, SPM, NH3, H2S) to check the statistical significance of each parameter as an independent variable and calculated tCO2e as the dependent variable. Excel Muti-variate linear regression method was then used to generate a predictive model for tCO2e and gaseous emission parameters. It presented a relationship between the emission from flared gas and air quality index.
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Xu, Hongyi, Ching-Hung Chuang, and Ren-Jye Yang. "Mixed-Variable Metamodeling Methods for Designing Multi-Material Structures." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59176.

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To establish metamodels for the multi-material structure design problems, the material selection of each component is considered as a categorical design variable. One challenging task is to establish an accurate mixed-variable metamodel. It is critical to reduce the prediction error of the mixed-variable metamodel in order to achieve a feasible design with superior performance in the metamodel-based optimization. This paper investigates two different strategies of mixed-variable metamodeling: “feature separating” strategy and “all-in-one” strategy. A supervised learning-aided method is proposed to improve the “feature separating” metamodels. The proposed method is compared with several existing mixed-variable metamodeling methods on three engineering benchmark problems to understand their relative merits. These methods include Neural Network (NN) regression, Classification and Regression Tree (CART) and Gaussian Process (GP). A new Polynomial Coefficient Metric is developed to quantify the adequacy of training data. This study provides insight and guidance for establishing proper metamodels on multi-material structural design problems.
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Zhang, Haocheng. "The Establishment of Multi-variable Linear Regression in Steam Sales." In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/aebmr.k.220307.137.

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Abele, Sebastian, and Michael Weyrich. "Supporting the regression test of multi-variant systems in distributed production scenarios." In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2016. http://dx.doi.org/10.1109/etfa.2016.7733652.

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Reports on the topic "Multi variate regression"

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Baete, Christophe, and Keith Parker. PR405-213601-R04 Validation of Digital Twins for Monitoring, Optimization, and Compliance of CP Systems. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2023. http://dx.doi.org/10.55274/r0012254.

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The digital twin used in this research is a mechanistic (or deterministic) computer model that represents the cathodic protection behavior of a pipeline network. The model is calibrated based on field data such that it mimics the real-world behavior of the pipeline. The objective of this research is to validate the accuracy of the digital twin model on three industry cases. First, the sensitivity of the independent variables (soil, coating and cathodic protection properties) that are the inputs of the digital twin was investigated during a literature study, a lab and field-based modeling on a theoretical pipeline network. A statistical analysis with the multi-variate adaptive regression spline method was performed to identify the most influencing independent variables on the cathodic protection modeling. Secondly, the accuracy of the digital twin model was validated on three real-world pipeline cases with a different degree of complexity. The digital twin models were calibrated based on the available data without any additional field testing. In two out of three cases an accuracy of approximately 90% was obtained between the simulated and measured pipe-to-soil ON and OFF potentials. Digital twin models with sufficient accuracy are used to make assessments on the cathodic protection effectiveness and risk of DC stray current interference. It supports systemic improvements to CP monitoring with reduced dependence on field collected operational data - which is about preventing corrosion. A sound digital twin model is used for endorsing integrity programs and ultimately for compliance reporting to the regulator. Related webinar.
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Alexander, Serena E., Mariela Alfonzo, and Kevin Lee. Safeguarding Equity in Off-Site Vehicle Miles Traveled (VMT) Mitigation in California. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2027.

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Historically, the State of California assessed the environmental impacts of proposed developments based on how it was projected to affect an area’s level of service (LOS). However, as LOS focused on traffic delays, many agencies simply widened roads, which was an ineffective way to reduce greenhouse gas emissions (GHGs). With the passage of Senate Bill (SB)743 in 2013, LOS was replaced by Vehicle Miles Traveled (VMT) as a more appropriate metric by which to gauge the environmental impacts of proposed development. Additionally, SB 743 presented an opportunity for off-site VMT mitigation strategies through banking and exchanges– allowing multiple development projects to fund a variety of strategies to reduce VMT elsewhere in the city or region. While the shift from LOS to VMT has generally been lauded, concerns remain about how to apply SB 743 effectively and equitably. This study aimed to: 1) understand how local governments are addressing this shift toward VMT while ensuring equity, including its approaches to off-site VMT mitigation; and 2) evaluate the various built environment factors that impact VMT, which should be considered by local governments, using both qualitative and quantitative research designs. The study posited that both micro and macro level aspects of the built environment needed to be considered when evaluating the impacts of proposed development on VMT, not only to ensure higher accuracy VMT models, but also because of the potential equity implications of off-site mitigation measures. Using multiple linear regression, the study shows that macroscale built environment features such as land use, density, housing, and employment access have a statistically significant impact on reducing VMT (35%), along with transit access (15%), microscale features such as sidewalks, benches, and trees (13%), and income (6%). More notably, a four-way interaction was detected, indicating that VMT is dependent on the combination of macro and micro level built environment features, public transit access, and income. Additionally, qualitative interviews indicate that transportation practitioners deal with three types of challenges in the transition to VMT impact mitigation: the lack of reliable, standardized VMT measure and evaluation tools; the lack of a strong legal foundation for VMT as a component of the California Environmental Quality Act (CEQA); and the challenge of distributing off-site VMT mitigation equitably. Overall, findings support a nuanced, multi-factor understanding of the context in which new developments are being proposed, both in terms of modeling VMT, but also when considering whether offsite mitigation would be appropriate. The results of this study can help California ensure equitable VMT mitigation that better aligns with the state’s climate goals.
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