scholarly journals Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuedong Chen ◽  
Qianying Zeng ◽  
Qiankun Song
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Expectation Maximization Algorithm ◽  
Simulated Data ◽  
Likelihood Method ◽  
Skew Normal Distribution ◽  
Linear Regression Models ◽  
Heavy Tailed Distributions ◽  
Random Term ◽  
Heavy Tailed

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-tdistribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing thel1penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.

scholarly journals Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline

2018 ◽  
Vol 8 (1) ◽  
pp. 135
Author(s):  
Mingao Yuan ◽  
Yue Zhang
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Regions ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
B Spline ◽  
Empirical Likelihood Method ◽  
Regression Parameters ◽  
Functional Linear Regression

In this paper, we apply empirical likelihood method to infer for the regression parameters in the partial functional linear regression models based on B-spline. We prove that the empirical log-likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi-square distributions. Our simulation shows that the proposed empirical likelihood method produces more accurate confidence regions in terms of coverage probability than the asymptotic normality method.

Empirical Likelihood Testing for Linear Instrumental Variable Regression Models

2014 ◽  
Vol 1008-1009 ◽  
pp. 1501-1504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Regression Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
Testing Method ◽  
Instrumental Variable Regression

Based on the empirical likelihood method, an instrumental variable based testing procedure is proposed for linear regression models with instrumental variables. The proposed testing method can attenuate the effect of endogeneity of covariates. Some simulations indicate that the proposed testing method is more powerful.

scholarly journals What Quantile Regression Does and Doesn't Do

2018 ◽  
Author(s):  
Sebastian Ernst Wenz
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Models ◽  
Simulated Data ◽  
Quantile Function ◽  
Linear Regression Models ◽  
Conditional Quantile ◽  
Correct Understanding ◽  
Mean Function ◽  
Conditional Quantile Function

Petscher and Logan (2014)’s description of quantile regression might mislead readers to believe it would estimate the relation between an outcome, y, and one or more predictors, x, at different quantiles of the unconditional distribution of y. However, quantile regression models the conditional quantile function of y given x just as linear regression models the conditional mean function. This article’s contribution is twofold: First, it discusses potential consequences of methodological misconceptions and formulations of Petscher and Logan (2014)’s presentation by contrasting features of quantile regression and linear regression. Secondly, it reinforces the importance of correct understanding of quantile regression in empirical research by illustrating similarities and differences of various quantile regression estimators and linear regression using simulated data.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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