A Combined Biased-Robust Estimator for Dealing with Influence and Collinearity in Regression PDF Download
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Author: James R. Simpson Publisher: ISBN: Category : Languages : en Pages : 29
Book Description
Regression analysis is a statistical tool that has earned widespread use in nearly all areas of endeavor seeking to fit a model to a set of data, Although there are several methods of estimating the model parameters, the least squares method is used most often because of its general acceptance, elegant statistical properties and ease of computation. Unfortunately, the mathematical elegance that makes least squares so popular depends on a number of fairly strong and many times unrealistic assumptions. The assumption that makes least squares so attractive in terms of hypothesis testing and confidence intervals on the parameter estimates is that the distribution of the errors is normal or Gaussian. This assumption can be violated if one or more sufficiently outlying observations are present in the data, resulting in less than optimal estimates of the parameters. The second problem that can ruin the accuracy of least squares estimates is correlated regressors. Highly correlated regressors can cause large variances in the estimates of the coefficients, sometimes resulting in incorrect levels of magnitude or even incorrect signs for the coefficients. The objective of this research is to develop a biased-robust regression estimator and determine how the method performs in the presence of nonnormal errors (outliers) and multicollinear regressor variables. To accomplish this major objective a number of investigative questions must be answered.
Author: James R. Simpson Publisher: ISBN: Category : Languages : en Pages : 29
Book Description
Regression analysis is a statistical tool that has earned widespread use in nearly all areas of endeavor seeking to fit a model to a set of data, Although there are several methods of estimating the model parameters, the least squares method is used most often because of its general acceptance, elegant statistical properties and ease of computation. Unfortunately, the mathematical elegance that makes least squares so popular depends on a number of fairly strong and many times unrealistic assumptions. The assumption that makes least squares so attractive in terms of hypothesis testing and confidence intervals on the parameter estimates is that the distribution of the errors is normal or Gaussian. This assumption can be violated if one or more sufficiently outlying observations are present in the data, resulting in less than optimal estimates of the parameters. The second problem that can ruin the accuracy of least squares estimates is correlated regressors. Highly correlated regressors can cause large variances in the estimates of the coefficients, sometimes resulting in incorrect levels of magnitude or even incorrect signs for the coefficients. The objective of this research is to develop a biased-robust regression estimator and determine how the method performs in the presence of nonnormal errors (outliers) and multicollinear regressor variables. To accomplish this major objective a number of investigative questions must be answered.
Author: Badi Hani Baltagi Publisher: ISBN: 0199940045 Category : Business & Economics Languages : en Pages : 705
Book Description
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Author: Mathias Harrer Publisher: CRC Press ISBN: 1000435636 Category : Mathematics Languages : en Pages : 500
Book Description
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Author: James R. Simpson
Author: James R. Simpson
Author: Esteban Walker
Author: 
Author: James Robert Simpson
Author: Steven Arthur Edelstein
Author: Ronald Gene Askin
Author: Arezoo Bagheri
Author: Badi Hani Baltagi
Author: Mathias Harrer
Author: