Statistical Analysis of Exponential Lifetimes Under an Integrated Type-Ii Interval Censoring Scheme

2002 ◽  
Vol 72 (6) ◽  
pp. 461-471 ◽  
Author(s):  
Siu-Keung Tse* ◽  
Hak-Keung Yuen ◽  
Chunyan Yang
Keyword(s):  
Statistical Analysis ◽  
Interval Censoring ◽  
Type Ii ◽  
Censoring Scheme

scholarly journals Statistical analysis of exponential lifetimes under an adaptive Type-II progressive censoring scheme

2009 ◽  
Vol 56 (8) ◽  
pp. 687-698 ◽  
Author(s):  
Hon Keung Tony Ng ◽  
Debasis Kundu ◽  
Ping Shing Chan
Keyword(s):  
Statistical Analysis ◽  
Type Ii ◽  
Progressive Censoring ◽  
Censoring Scheme

scholarly journals Analysis of soil radon data in earthquake precursory studies

2014 ◽  
Vol 57 (5) ◽  
Author(s):  
Hari Prasad Jaishi ◽  
Sanjay Singh ◽  
Raghavendra Prasad Tiwari ◽  
Ramesh Chandra Tiwari
Keyword(s):  
Statistical Analysis ◽  
Multiple Regression ◽  
Radon Concentration ◽  
Seismic Activity ◽  
Meteorological Parameters ◽  
Regression Method ◽  
Seismic Zone ◽  
Type Ii ◽  
Soil Radon ◽  
Multiple Regression Method

<p>Soil radon data were recorded at two selected sites along Mat fault in Mizoram (India), which lies in the highest seismic zone in India. The study was carried out during July 2011 to May 2013 using LR-115 Type II films. Precursory changes in radon concentration were observed prior to some earthquakes that occurred around the measuring sites. Positive correlation was found between the measured radon data and the seismic activity in the region. Statistical analysis of the radon data together with the meteorological parameters was done using Multiple Regression Method. Results obtained show that the method employed was useful for removing the effect of meteorological parameters and to identify radon maxima possibly caused by seismic activity.</p>

scholarly journals A Progressive Interval Type-I Censored Life Test Plan for Rayleigh Distribution

2019 ◽  
Vol 48 (3) ◽  
pp. 76-86
Author(s):  
Arun Kaushik
Keyword(s):  
Rayleigh Distribution ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Type I ◽  
Test Plan ◽  
Type I Censoring ◽  
Progressive Type ◽  
Interval Type ◽  
Censoring Scheme ◽  
Two Parameter

In this paper, we have considered the problem of optimal inspection times for the progressive interval type-I censoring scheme where uncertainty in the process is governed by the two-parameter Rayleigh distribution. Here, we also introduced some optimality criterion and determined the optimum inspection times, accordingly. The effect of the number of inspections and choice of optimally spaced inspection times based on the asymptotic relative efficiencies of the maximum likelihood estimates of the parameters are also investigated. Further, we have discussed the optimal progressive type-I interval censoring plan when the inspection times and the expected proportions of total failures in the experiment are under control.

In Reply: Statistical Power

PEDIATRICS ◽  
1989 ◽  
Vol 83 (4) ◽  
pp. 634-634
Author(s):  
JOHN S. LOVERING
Keyword(s):  
Statistical Analysis ◽  
Statistical Power ◽  
Type Ii ◽  
Type Ii Error ◽  
Negative Results ◽  
Mean Variance

Dr. Mauro is obviously knowledgeable in the area of statistical analysis and raises a valid point regarding the importance of evaluating the likelihood of a type II error in studies with negative results. Although one does not wish to detract from the main point of a study with extensive details of the statistical analysis (two pages in this case), some readers may desire more mathematical information than values of mean, variance, t, and P, and do not wish to make their own calculations, to reassure themselves that a reasonable conclusion has been drawn by the authors and their statisticians.

scholarly journals Different Approaches to Estimation of the Gompertz Distribution under the Progressive Type-II Censoring Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Kyeongjun Lee ◽  
Jung-In Seo
Keyword(s):  
Nuisance Parameter ◽  
Estimation Method ◽  
Real Data ◽  
Type Ii ◽  
Gompertz Distribution ◽  
Type Ii Censoring ◽  
Scale Parameters ◽  
Progressive Type ◽  
Censoring Scheme ◽  
Progressive Type Ii Censoring

This paper provides an estimation method for an unknown parameter by extending weighted least-squared and pivot-based methods to the Gompertz distribution with the shape and scale parameters under the progressive Type-II censoring scheme, which induces a consistent estimator and an unbiased estimator of the scale parameter. In addition, a way to deal with a nuisance parameter is provided in the pivot-based approach. For evaluation and comparison, the Monte Carlo simulations are conducted, and real data are analyzed.

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