A Computational Approach Test for Comparing Two Linear Regression Models with Unequal Variances

In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

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.

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A Note on the Estimation of Linear Regression Models with Heteroskedastic Measurement Errors

SSRN Electronic Journal ◽

10.2139/ssrn.295567 ◽

2002 ◽

Cited By ~ 2

Author(s):

Daniel G. Sullivan

Keyword(s):

Linear Regression ◽

Measurement Errors ◽

Regression Models ◽

Linear Regression Models

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

Anais do 14. Congresso Brasileiro de Inteligência Computacional ◽

10.21528/cbic2019-3 ◽

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

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

Gadjah Mada International Journal of Business ◽

10.22146/gamaijb.5633 ◽

2003 ◽

Vol 5 (3) ◽

pp. 363 ◽

Cited By ~ 1

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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