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Household, sociodemographic, building and land cover factors affecting residential summer electricity consumption: A systematic statistical study in Phoenix, AZ

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ENERGY 2024年 313卷

作者： Ramirez-Aguilar, Edwin Alejandro Sailor, David J. Wentz, Elizabeth A. Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA Arizona State Univ Urban Climate Res Ctr Tempe AZ USA

Understanding determinants of residential electricity consumption is crucial for urban sustainability efforts for planners and policy makers to develop targeted strategies to lower energy use, reduce greenhouse gas emissions, and to increase community resilience. This study presents a systematic approach to build an interpretable multivariate linear model, addressing challenges like outlier detection, multicollinearity, non-normality, and heteroscedasticity. Using 2019 summer residential electricity data for 426 census tracts in Phoenix and 30 variables, the approach involves (1) addressing multicollinearity and regression outliers through Variance Inflation Factor and studentized residual analysis, (2) comparing an automatic variable selection method with Ridge, Lasso, and Elastic Net regression, (3) evaluating the final model, and (4) interpreting variable effects. Critical findings reveal multicollinearity in land cover and racial variables, while 21 census tracts on the urban periphery exhibit outliers with unique features. Variable selection demonstrates the significance of household and building information in influencing residential electricity consumption. Household variables alone account for 84 % of electricity usage variation. Incorporating building information and land cover variables reduces errors by 35 % and 26 % respectively, emphasizing the significance of including household characteristics as predictors or control variables when modeling electricity consumption. A final model with 93 % explanatory power enables precise predictions.

关键词： Residential electricity consumption multivariate linear model Statistical modeling Variable selection Regularization Arizona

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Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery-Active Sample Selection for model Transfer

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DRONES 2022年第3期6卷 73-73页

作者： Franceschini, Marston H. D. Becker, Rolf Wichern, Florian Kooistra, Lammert Wageningen Univ & Res Lab Geoinformat Sci & Remote Sensing POB 47 NL-6700 AA Wageningen Netherlands Rhine Waal Univ Appl Sci Fac Life Sci Marie Curie Str 1 D-47533 Kleve Germany Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression-PLSR) for grassland dry matter (DM;R-2 = 0.92, RMSE = 3.25 dt ha(-1)), nitrogen (N) content in % of DM (R-2 = 0.58, RMSE = 0.27%) and N-uptake (R-2 = 0.91, RMSE = 6.50 kg ha(-1)). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after mod

关键词： Vis-NIR spectroscopy hyperspectral imagery nitrogen management pasture growth monitoring multivariate linear model uncertainty analysis

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The generalized symmetric eigenproblem in multivariate statistical models

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Communications in Statistics - Theory and Methods 1977年第3期6卷 277-288页

The solution of the generalized symmetric eigenproblem Ax = λBx is required in many multivariate statistical models, viz. canonical correlation, discriminant analysis, multivariate linear model, limited information maximum likelihoods. The problem can be solved by two efficient numerical algorithms: Cholesky decomposition or singular value decomposition. Practical considerations for implementation are also discussed.

关键词： canonical analysis statistical computations statistical packages discriminant analysis multivariate linear model LIML Cholesky Householder singular value

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Crop yield estimation at district level for agricultural seasons 2014 in Rwanda

African Journal of Applied Statistics

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African Journal of Applied Statistics 2016年第1期3卷 69-90页

作者： Innocent NGARUYE Dietrich VON ROSEN Martin SINGULL

In this paper, we discuss an application of Small Area Estimation (SAE) techniques under a multivariate linear regression model for repeated measures data to produce district level estimates of crop yield for beans which comprise two varieties, bush beans and climbing beans in Rwanda during agricultural seasons 2014. By using the micro data of National Institute of Statistics of Rwanda (NISR) obtained from the Seasonal Agricultural Survey (SAS) 2014 combined with the data from the Crop Assessment Survey 2013, we derive efficient estimates which show considerable gain. The considered model and its estimates may be useful for policy-makers or for further analyses. Dans cet article, nous abordons une application des techniques d'estimation sur petits domaines en utilisant le model de régression linéaire multivariée pour des données de mesures répétées en vue de produire des estimations du rendement de récolte de haricots bruts et haricots grimpants pour les saisons agriculturales 2014 au Rwanda. En utilisant les microdonn´ees de l'Institut National des Statistiques au Rwanda (NISR) obtenues de l'ênquete agricultural saisonier combinées avec les données de l'ênquete d'évaluation des cultures, année 2013, nous déduisons des estimations efficaces avec un gain considérable. Le modèle considéré et ses estimations peuvent être utiles aux décideurs politiques ou à une analyse plus approfondie.

关键词： 62D05 62F10 62H12 crop yield model-based estimates multivariate linear model small area estimation

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Multiple Use Confidence Regions in multivariate Calibration

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Journal of the American Statistical Association 1997年第439期92卷 1141-1150页

作者： Mathew, Thomas Wenxing Zha Professor Department of Mathematics and Statistics University of Maryland Baltimore County Baltimore MD. Statistical Analyst Orbiting Astronomy Observatory Corporation Greenbelt MD 20770

The problem of multivariate calibration is considered in the setup where a normally distributed response variable is related to an explanatory variable through a multivariate linear model. The variance covariance matrix of the response variable is assumed to be a multiple of the identity matrix. The calibration data that is, data obtained on the response variable corresponding to known values of the explanatory variable, are to be used for the construction of confidence regions for unknown values of the explanatory variable. The calibration problem addressed in this article deals with the construction of multiple use confidence regions: that is, the calibration data will be used repeatedly in order to construct a sequence of confidence regions for a sequence of unknown values of the explanatory variable, Such a procedure is characterized using two coverage probabilities, say 1 - α and 1 - β, Given that the confidence regions are constructed using the same calibration data, the proportion of confidence regions that include the true values of the corresponding parameters is to be at least 1 - β. The probability that the calibration data will provide 100(1 - β)%coverage is to be at least 1 - α. A multiple use confidence region is constructed using a pivot statistic that is a natural choice. The procedure is then generalized to linear models where the explanatory variable enters the model nonlinearly, such as in a polynomial regression model. The computational aspects and the practical implementation of our confidence region are illustrated in detail using two examples. [ABSTRACT FROM AUTHOR]

关键词： Conservative confidence region Coverage probability multivariate linear model Noncentral chi-square.

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Asymptotic expansion for Lack-of-Fit test under nonnormality

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Hiroshima Mathematical Journal 2006年第1期36卷 29-38页

作者： Chieko Matsumoto Hirofumi Wakaki

It is shown that the Lack-of-Fit test can be considered as the likelihood ratio test on the mean structure for some linear model. The asymptotic expansion of the null distribution of the test statistic is derived up t... 详细信息

关键词： 62H10 62H20 asymptotic expansion lack-of-fit test multivariate linear model nonnormality null distribution

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A study on the measurement for forest ecological benefit

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Journal of Forestry Research 2000年第1期11卷 37-40页

作者： Zhang Jie Li Xueyao Jiang Qiulai Li Changsheng Li Peng Dong Denfeng Lin Lisha Xu Wenting 黑龙江省尚志国有林场管理局黑龙江尚志牡丹江林业管理局黑龙江牡丹江黑龙江穆陵林业局黑龙江穆陵东北林业大学黑龙江哈尔滨

The indexes of dependent variables of the measurement on the forest ecological benefits were defined according to the analysis of the multiple ecological benefits of forest. This indexes system includes watereserving, soil and water conservation, wind and sand suppression, microclimate improvement, carbon dioxide assimilation, atmosphere purification, flood and drought mitigation, tourism resource and wild creature protection benefits. The main factors from the numerous factors that affect dependent variables were chosen as independent variables. At last, a multivariate linear model was established for measurement of forest ecological benefit. With this multivariate linear model the forest ecological benefit of China was calculated. The forest ecological benefit of China is 723816 million yuan per year, which equals to 23.07% of the gross domestic product of China.

关键词： forest ecological benefit measurement index system multivariate linear model

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Least Squares Estimation of Covariance Matrices in Balanced multivariate Variance Components models

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Journal of the American Statistical Association 1991年第414期86卷 388-395页

作者： Calvin, James A. Dysktra, Richard L. Assistant Professor Department of Statistics University of Iowa Iowa City IA 52242. Professor Department of Statistics University of Iowa Iowa City IA 52242

The problem of estimating covariance matrices in balanced multivariate variance components models is discussed. As with univariate models, it is possible for the traditional estimators, based on differences of the mean square matrices, to produce estimates that are outside the parameter space. In fact, in many cases it is extremely likely that traditional estimates of the covariance matrices will not be nonnegative definite (nnd), In this article we develop an iterative estimation procedure, satisfying a least squares criterion, that is guaranteed to produce nnd estimates of the covariance matrices, discuss the speed of convergence, and provide an example to show how the estimates change. [ABSTRACT FROM AUTHOR]

关键词： Fenchel duality Isotonic regression Löwner ordering multivariate linear model Nonnegative definite Ordinary least squares Unweighted least squares Wishart density.

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Vec and vech operators for matrices, with some uses in jacobians and multivariate statistics

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Canadian Journal of Statistics 1979年第1期7卷 65-81页

作者： Henderson, Harold V. Searle, S.R. Biometrics Unit Cornell University Ithaca New York 14853 Warren Hall United States

The vec of a matrix X stacks columns of X one under another in a single column;the vech of a square matrix X does the same thing but starting each column at its diagonal element. The Jacobian of a one‐to‐one transformation X → Y is then ∣∣∂(vecX)/∂(vecY) ∣∣ when X and Y each have functionally independent elements;it is ∣∣ ∂(vechX)/∂(vechY) ∣∣ when X and Y are symmetric;and there is a general form for when X and Y are other patterned matrices. Kronecker product properties of vec(ABC) permit easy evaluation of this determinant in many cases. The vec and vech operators are also very convenient in developing results in multivariate statistics. Copyright © 1979 Statistical Society of Canada

关键词： determinants eigenvalues fourth moments in general linear model Jacobians linear matrix equations matrix differentiation multivariate linear model patterned matrices singular value decomposition transformations Vec operator vech operator Wishart distribution

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An algorithm for restricted maximum likelihood estimation in balanced multivariate variance components models

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Journal of Statistical Computation and Simulation 1992年第3-4期40卷 233-246页

作者： Calvin, James A. Department of Statistics at Texas A&M University Old Gold Fellowship from the University of Iowa United States Department of Statistics Actuarial Science University of Iowa United States

Restricted maximum likelihood (REML) estimation of variance components in multivariate linear models is a computationally intensive task. Calvin and Dykstra (1991a) have developed a general maximum likelihood estimation algorithm for a product of Wishart densities when the parameter matrices of the Wishart distributions follow a partial order of the Lowner type (Lowner, 1934). The algorithm is iterative in nature and does not require the computation of derivatives. It is guaranteed to converge to the correct solution and, for an important class of models, can be applied to the problem of REML estimation of variance components. In this paper, we define the class of models for which the algorithm is applicable. This class includes all two-factor models and all nested models as special cases. We also relate the general algorithm to the problem of REML estimation for balanced multivariate variance components models. An implementation is supplied in the Appendix and computational aspects of the algorithm are also discussed. © 1992, Taylor & Francis Group, LLC. All rights reserved.

关键词： Isotonic regression. Fenchel duality multivariate linear model REML

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