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Predicting the role of socio-economic indices for the Human Development Index based on a multivariate regression model

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DISCOVER PUBLIC HEALTH 2025年第1期22卷 1-11页

作者： Alimoradi, Zainab Griffiths, Mark D. Alijanzadeh, Mehran Qazvin Univ Med Sci Res Inst Prevent Noncommun Dis Social Determinants Hlth Res Ctr Qazvin Iran Nottingham Trent Univ Psychol Dept Int Gaming Res Unit Nottingham England

Background Social and economic indicators of countries at the global level can reveal both weak and strong achievements concerning specific countries on a wide range of indices. The purpose of the present study was to investigate the correlations between social and economic indicators and the Human Development Index (HDI), a summary composite measure of a country's average wellbeing. Methods Secondary analysis was conducted between April and July 2022. Six variables of the HDI (i.e., the Gini Coefficient Index [GCI], Multidimensional Poverty Index [MPI], Research and Development Percentage Index of gross domestic product [R&D], infant mortality rate (IMR), and Gender Development Index [GDI]) were investigated across 189 countries in six continents. Data were analyzed using a multivariate regression model. Results The average HDI in the countries of the world was equal to 0.72 (SD +/- 0.14), with the highest HDI score in Europe (0.87 +/- 0.06;p < .001). Europe also had the highest R&D (1.34 [SD +/- 1.02];p < .001) and GDI indicators (0.98 [SD +/- 0.02];p < .001). Africa had the highest infant mortality (41.62 [SD +/- 18.93];p < .001) and highest MPI (0.230 [SD +/- 0.166];p < .001). America had the highest GCI (44.10 [SD +/- 6.27];p < .001). Findings indicated that countries with a higher HDI had better social and economic indicators (p < .001). There was a correlation between all selected indices with the HDI. The highest (negative) correlation was observed between IMR and HDI (r = - 0.885). The multivariate regression model showed IMR and the MPI were significant predictors of HDI and explained 84.7% of variance. Conclusion The two country indicators of IMR and MPI are good predictors of a country's HDI.

关键词： Human Development Index Socio-economic indicators Infant mortality multivariate regression model

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multivariate regression model for Industrial Process Measurement Based on Double Locally Weighted Partial Least Squares

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第7期69卷 3962-3971页

作者： Chen, Junming Yang, Chunhua Zhou, Can Li, Yonggang Zhu, Hongqiu Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Cent South Univ Sch Automat State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China

multivariate regression models are commonly used in industrial process measurements. Locally weighted partial least squares (LWPLS) is a just-in-time learning (JITL) method for nonlinear multivariate regression models. However, it considers only the sample importance. In this article, a novel JITL multivariate regression model termed double LWPLS (D-LWPLS) is proposed to solve complex nonlinear problems and promote the model accuracy. Both the sample importance and variable importance are taken into consideration for the weighted model. Variable contribution is modified according to the variable importance. Sample similarity measurement is a significant basis for the model sample selection and sample weight calculation. The traditional sample similarity measurement methods take only the input information of samples as the index. In the D-LWPLS, a weighted similarity measurement is employed. The information on both input and output is considered by weighting the input variables according to the correlation between them. The effectiveness and the flexibility of the D-LWPLS model are demonstrated by the prediction results of a numerical example, a low-dimensional data set of an industrial process, and a high-dimensional data set of UV-Vis spectra of a polymetallic ion solution, which represent three kinds of nonlinear problems of classical, time-varying, and blind background fluctuation.

关键词： multivariate regression Weight measurement Input variables Analytical models Eigenvalues and eigenfunctions Spectral analysis Adaptation models Double locally weighted partial least squares (D-LWPLS) just-in-time learning (JITL) multivariate regression model nonlinear problem partial least squares (PLS)

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On efficiency of some restricted estimators in a multivariate regression model

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STATISTICAL PAPERS 2023年第2期64卷 617-642页

作者： Nkurunziza, Severien Univ Windsor Dept Math & Stat 401 Sunset Ave Windsor ON N9B 3P4 Canada

In this paper, we study a constrained estimation problem in a multivariate measurement error regression model. In particular, we derive the joint asymptotic normality of the unrestricted estimator (UE) and the restricted estimators (REs) of the matrix of the regression coefficients. The derived result holds under the hypothesized restriction as well as under the sequence of alternative restrictions. In addition, we establish Asymptotic Distributional Risk for the UE and the REs and compare their relative performance. It is established that near the restriction, the restricted estimators (REs) perform better than the UE. But the REs perform worse than the UE when one moves far away from the restriction. Further, we explore by simulation the performance of the shrinkage estimators (SEs). The numerical findings corroborate the established theoretical results about the relative risk dominance between the REs and the UE. The findings also show that near the restriction, the REs dominate SEs but as one moves far away from the restriction, REs perform poorly while SEs dominate always the UE.

关键词： ADR Asymptotic normality Measurement error multivariate regression model Restricted estimator Unrestricted estimator

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Some remarks to multivariate regression model

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Applications of Mathematics 2006年第6期51卷 565-582页

作者： Kubáček, Lubomír Department of Mathematical Analysis and Applications of Mathematics Faculty of Science Palacký University Tomkova 40 Olomouc 771 46 Czech Republic

Some remarks to problems of point and interval estimation, testing and problems of outliers are presented in the case of multivariate regression model. © 2006 Mathematical Institute, Academy of Sciences of Czech ... 详细信息

关键词： multivariate regression model outliers variance components Wishart matrix

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Robust estimation of multivariate regression model

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STATISTICAL PAPERS 2009年第1期50卷 81-100页

作者： Li, Jiantao Zheng, Min Peking Univ Sch Math Sci Beijing 100871 Peoples R China

This paper studies robust estimation of multivariate regression model using kernel weighted local linear regression. A robust estimation procedure is proposed for estimating the regression function and its partial derivatives. The proposed estimators are jointly asymptotically normal and attain nonparametric optimal convergence rate. One-step approximations to the robust estimators are introduced to reduce computational burden. The one-step local M-estimators are shown to achieve the same efficiency as the fully iterative local M-estimators as long as the initial estimators are good enough. The proposed estimators inherit the excellent edge-effect behavior of the local polynomial methods in the univariate case and at the same time overcome the disadvantages of the local least-squares based smoothers. Simulations are conducted to demonstrate the performance of the proposed estimators. Real data sets are analyzed to illustrate the practical utility of the proposed methodology.

关键词： Local M-estimator Local polynomial regression multivariate regression model One-step Robustness

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A multivariate regression model for the Assessment of Solar Radiation in the Senegalese Territories 1

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1st International Conference on Innovation and Interdisciplinary Solutions for Underserved Areas (InterSol) / 6th National Colloquium on Computer Research and Applications (CNRIA)

作者： Wane, Ousmane Navarro, A. A. Ramirez, L. Valenzuela, R. X. Vindel, Jose M. Ferrera Cobos, F. Kebe, Cheikh M. F. Zarzalejo, L. F. Ecole Super Polytech UCAD CIFRES BP 5085 Dakar Senegal CIEMAT Energy Dept Renewable Energy Div Av Complutense 40 E-28040 Madrid Spain

ISBN: (数字)9783319729657

ISBN: (纸本)9783319729657;9783319729640

Senegal has a great solar potential, so it could be used to shift from a diesel-based power generation to cheaper renewable energy resources. To exploit this inexhaustible natural resource, the global horizontal irradiation remains one of the key parameters for any solar energy project at a given location. This work establishes a multiple linear regression approach to estimate the solar radiation in the Senegalese territories using the information of the global network of weather geostationary satellites (Meteosat and GOES), satellites database and the ground measurement data available in the website of the World Radiation Data Center (WRDC) as inputs to the model. Jointly a set of multivariate regression models, a statistical analysis between Meteonorm data and outputs of different linear combinations are presented in this work, which also gives the opportunity to appreciate the precision and consistency of each solar radiation model on different locations in the study area.

关键词： Solar radiation Global horizontal irradiation multivariate regression model Satellites database

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An Improved Estimation in regression Parameter Matrix in multivariate regression model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2012年第13-14期41卷 2305-2320页

作者： Chitsaz, S. Ahmed, S. Ejaz Univ Windsor Dept Math & Stat Windsor ON N9B 3P4 Canada Brock Univ Dept Math St Catharines ON L2S 3A1 Canada

We consider shrinkage and preliminary test estimation strategies for the matrix of regression parameters in multivariate multiple regression model in the presence of a natural linear constraint. We suggest a shrinkage and preliminary test estimation strategies for the parameter matrix. The goal of this article is to critically examine the relative performances of these estimators in the direction of the subspace and candidate subspace restricted type estimators. Our analytical and numerical results show that the proposed shrinkage and preliminary test estimators perform better than the benchmark estimator under candidate subspace and beyond. The methods are also applied on a real data set for illustrative purposes.

关键词： Candidate sub model estimator Candidate subspace multivariate regression model Preliminary test and shrinkage estimator

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High-dimensional consistencies of KOO methods in multivariate regression model and discriminant analysis

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JOURNAL OF multivariate ANALYSIS 2022年 188卷

作者： Fujikoshi, Yasunori Hiroshima Univ Dept Math Higashihiroshima Japan

In this paper, we review recent developments in high-dimensional consistencies of KOO methods for selection of variables in multivariate regression models and discriminant analysis models. The KOO methods considered are mainly based on general information criteria, but we take up also KOO methods based on some other selection methods. Some references are given for high-dimensional consistencies in some other multivariate models. (C) 2021 Published by Elsevier Inc.

关键词： Discriminant analysis General information criteria High-dimensional consistency KOO method multivariate regression model Selection of variables

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Predictions in time series using multivariate regression models

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JOURNAL OF TIME SERIES ANALYSIS 2001年第3期22卷 365-373页

作者： Stulajter, F Comenius Univ Bratislava 81806 Slovakia

Predictions in time series using multivariate regression models are studied with respect to their mean squared errors. Two new methods of prediction are proposed: the simple one and the method based on the kriging theory, The mean squared errors of these predictions are computed and it is shown that the first one can be regarded as a special case of the kriging approach.

关键词： multivariate regression model best linear unbiased predictor mean squared error of predictor

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Diagnostic checking for multivariate regression models

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JOURNAL OF multivariate ANALYSIS 2008年第9期99卷 1841-1859页

作者： Zhu, Lixing Zhu, Ruoqing Song, Song Hong Kong Baptist Univ Hong Kong Hong Kong Peoples R China Bowling Green State Univ Bowling Green OH 43403 USA Humboldt Univ D-1086 Berlin Germany

Diagnostic checking for multivariate parametric models is investigated in this article. A nonparametric Monte Carlo Test (NMCT) procedure is proposed. This Monte Carlo approximation is easy to implement and can automatically make any test procedure scale-invariant even when the test statistic is not scaleinvariant. With it we do not need plug-in estimation of the asymptotic covariance matrix that is used to normalize test statistic and then the power performance can be enhanced. The consistency of NMCT approximation is proved. For comparison, we also extend the score type test to one-dimensional cases. NMCT can also be applied to diverse problems such as a classical problem for which we test whether or not certain covariables in linear model has significant impact for response. Although the Wilks lambda, a likelihood ratio test, is a proven powerful test, NMCT outperforms it especially in non-normal cases. Simulations are carried out and an application to a real data set is illustrated. (C) 2008 Elsevier Inc. All rights reserved.

关键词： multivariate regression model Goodness-of-fit Wilks lambda Score tests Nonparametric Monte Carlo approximation

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