A composite logistic regression approach for ordinal panel data regression

 

Table Of Contents


Chapter ONE

INTRODUCTION

  • 1.1Introduction
  • 1.2Background of Study
  • 1.3Problem Statement
  • 1.4Objective of Study
  • 1.5Limitation of Study
  • 1.6Scope of Study
  • 1.7Significance of Study
  • 1.8Structure of the Research
  • 1.9Definition of Terms

Chapter TWO

LITERATURE REVIEW

  • 2.1Overview of Logistic Regression
  • 2.2Ordinal Panel Data Regression
  • 2.3Composite Approach in Statistics
  • 2.4Previous Studies on Composite Logistic Regression
  • 2.5Applications of Ordinal Panel Data Regression
  • 2.6Challenges in Logistic Regression Analysis
  • 2.7Comparison of Different Regression Techniques
  • 2.8Advantages of Composite Logistic Regression Approach
  • 2.9Disadvantages of Composite Logistic Regression Approach
  • 2.10Future Trends in Logistic Regression Analysis

Chapter THREE

RESEARCH METHODOLOGY

  • 3.1Research Methodology Overview
  • 3.2Data Collection Methods
  • 3.3Sampling Techniques
  • 3.4Variable Selection Process
  • 3.5Model Specification
  • 3.6Data Analysis Techniques
  • 3.7Validation Methods
  • 3.8Ethical Considerations in Research

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

  • 4.1Data Analysis and Interpretation
  • 4.2Descriptive Statistics of Variables
  • 4.3Regression Results and Coefficients
  • 4.4Model Fit and Goodness of Fit Tests
  • 4.5Discussion on Findings
  • 4.6Comparison with Existing Studies
  • 4.7Implications of Results
  • 4.8Recommendations for Future Research

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

  • 5.1Summary of Findings
  • 5.2Conclusion of the Study
  • 5.3Contributions to the Field
  • 5.4Limitations of the Research
  • 5.5Future Research Directions

Project Abstract

<p> We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied. As a result, the new method allows the unobserved subject effects to be correlated with the observed predictors in an arbitrary manner. Computationally, the new method is able to profile out unobserved subject effects in a very neat manner. This not only makes computational implementation very easy but also makes theoretical treatment straightforward. In particular, we show theoretically that the resulting estimator is n-consistent and asymptotically normal. Both simulations and a real example are reported to demonstrate the usefulness of the new method. <br></p>

Project Overview

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