Titirat Kittipichayaumporn. Confidence intervals for the signal-to-noise ratio of a log-normal distribution based on the normal approximation. Master's Degree(Applied Statistics). Thammasat University. Thammasat University Library. : Thammasat University, 2021.
Confidence intervals for the signal-to-noise ratio of a log-normal distribution based on the normal approximation
Abstract:
The objective of this research is to construct the confidence intervals (CIs) for the signal-to-noise ratio (SNR) of a log-normal distribution. The proposed CIs are based on the normal approximation and the delta methods. Three point estimators for the SNR used in this study are the naive estimator, logarithmic estimator and the special estimator. Naive estimator can be applied to any distribution (not necessary to the log-normal distribution). In addition, the special estimator works only for the log-normal distribution. There are five approaches for constructing CIs which are (i) CI for the SNR based on the naive estimator constructed from any distribution, (ii) logarithmic CI for the SNR based on the naive estimator constructed from any distribution, (iii) CI for the SNR based on the naive estimator constructed from log-normal distribution, (iv) logarithmic CI for the SNR based on the naive estimator constructed from log-normal distribution, and (v) CI for the SNR based on the special estimator constructed from log-normal distribution. The efficacy of all approaches is determined in terms of the coverage probability and average width. Monte Carlo simulation method is applied to investigate the performance of all five CIs. These CIs were applied to real data in terms of the SNR of the survival times of small-cell lung cancer (SCLC) patients in Canada and real data of the daily particulate matters 2.5 (PM2.5) level in Bangkok, Thailand
Thammasat University. Thammasat University Library