Linear Affine Estimation in Misspecified Linear Regression Models Using Fuzzy Prior Information

Statistics ◽  
1998 ◽  
Vol 32 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Bernhard F. Arnold ◽  
Peter Stahlecker
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Prior Information ◽  
Linear Regression Models ◽  
Affine Estimation

scholarly journals Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Manickavasagar Kayanan ◽  
Pushpakanthie Wijekoon
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Prior Information ◽  
Estimation Procedure ◽  
Regression Coefficients ◽  
Linear Regression Models ◽  
Monte Carlo Simulation Study ◽  
Regression Parameters ◽  
Linear Restrictions

Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the predictor variables. Since LASSO is unstable under high multicollinearity, the elastic-net (Enet) estimator has been used to overcome this issue. According to the literature, the estimation of regression parameters can be improved by adding prior information about regression coefficients to the model, which is available in the form of exact or stochastic linear restrictions. In this article, we proposed a stochastic restricted LASSO-type estimator (SRLASSO) by incorporating stochastic linear restrictions. Furthermore, we compared the performance of SRLASSO with LASSO and Enet in root mean square error (RMSE) criterion and mean absolute prediction error (MAPE) criterion based on a Monte Carlo simulation study. Finally, a real-world example was used to demonstrate the performance of SRLASSO.

Further results on estimating linear regression models with partial prior information

1988 ◽  
Vol 5 (1) ◽  
pp. 49-57 ◽  
Author(s):  
A.K. Kashyap ◽  
P.A.V.B. Swamy ◽  
J.S. Mehta ◽  
R.D. Porter
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Prior Information ◽  
Linear Regression Models

scholarly journals An Assessment of the Effects of Prior Distributions on the Bayesian Predictive Inference

2016 ◽  
Vol 5 (5) ◽  
pp. 31
Author(s):  
Azizur Rahman ◽  
Junbin Gao ◽  
Catherine D'Este ◽  
Syed Ejaz Ahmed
Keyword(s):  
Statistical Analysis ◽  
Linear Regression ◽  
Regression Models ◽  
Prior Information ◽  
Predictive Inference ◽  
Shape Parameters ◽  
Linear Regression Models ◽  
Prior Distributions ◽  
Noninformative Prior ◽  
Observable Data

Predictive inference is one of the oldest methods of statistical analysis and it is based on observable data. Prior information plays an important role in the Bayesian methodology. Researchers in this field are often subjective to exercise noninformative prior. This study tests the effects of a range of prior distributions on the Bayesian predictive inference for different modelling situations such as linear regression models under normal and Student-t errors. Findings reveal that different choice of priors not only provide different prediction distributions of the future response(s)  but also change the location and/or scale or shape parameters of the prediction distributions.

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.

Sign in / Sign up

Export Citation Format

Share Document