On the Asymptotic Efficiency of the Empirical Characteristic Function Estimator in the Exponential Proportional Hazards Model

This dataset contains all replication materials for the article: "Characteristic Function Method via the Dirac Delta Function: Parameter Estimation for Normal and Exponential Distributions under Random Censoring" (submitted). The repository includes: 1. Data: Leukemia remission data from Freireich et al. (1963) as reproduced in Kalbfleisch & Prentice (2002), Table 1.1. Two CSV files are provided: leukemia_6mp.csv (6-MP treatment group, n=21, 9 events) and leukemia_placebo.csv (placebo group, n=21, all events). 2. Code: Python scripts (numpy, scipy, pandas) that reproduce all Monte Carlo tables and verify every theoretical result in the paper: - Exact bias and variance formulas for the exponential PHM estimator (Theorem 6.6). - Asymptotic normality and efficiency (Theorem 7.2). - Weibull PHM extension (Theorem 7.5). - Cauchy MCF vs ML comparison (Table 4). - Real data analysis (Section 7.3). - Fisher information matrix verification. 3. README.md: Detailed instructions, file descriptions, dependency list, and reproducibility notes. 4. requirements.txt: Python package dependencies (numpy, scipy, pandas). All simulations use fixed random seeds (seed=42) for full reproducibility. Results match the tables and theorems in the paper to Monte Carlo accuracy.