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Now showing items 21-26 of 26
Maximum Likelihood Estimation in the Inverse Weibull Distribution with Type II Censored Data
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Horizon Research Publishing
, 2022 , Article)
We consider maximum likelihood estimation for the parameters and certain functions of the parameters in the Inverse Weibull (IW) distribution based on type II censored data. The functions under consideration are the Mean ...
An attribute control chart for the inverse Weibull distribution under truncated life tests
(
Elsevier Ltd
, 2022 , Article)
Statistical Process Control (SPC) is applied to monitor production processes in order to discover any problems or issues that may arise during the production process and to help in finding solutions for these issues. In ...
Large sample and bootstrap intervals for the gamma scale parameter based on grouped data
(
Wayne State University
, 2006 , Article)
Interval estimation of the scale parameter of the gamma distribution using grouped data is considered in this article. Exact intervals do not exist and approximate intervals are needed Recently, Chen and Mi (2001) proposed ...
Inference Based on New Pareto-Type Records With Applications to Precipitation and Covid-19 Data
(
International Academic Press
, 2023 , Article)
We consider estimation and prediction of future records based on observed records from the new Pareto type distribution proposed recently by Bourguignon et al. (2016), ¡°M. Bourguignon, H. Saulo, R. N. Fernandez, A new ...
Inference in simple regression for the intercept utilizing prior information on the slope
(
Wayne State University
, 2011 , Article)
Shrinkage type estimators are developed for the intercept parameter of a simple linear regression model and the case when it is suspected a priori that the slope parameter is equal to some specific value is considered. ...
A simple method for finding empirical likelihood type intervals for the ROC curve
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Wayne State University
, 2007 , Article)
Interval estimation of the ROC curve is considered using the empirical likelihood techniques. Suggested is a procedure that is very simple computationally and avoids the constrained optimization problems usually faced with ...