Sequential fixed width confidence intervals for regression parameters from censored data with a discrete covariate

1995 ◽  
Vol 14 (2) ◽  
pp. 143-156 ◽  
Author(s):  
Mohammad H. Rahbar
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Regression Parameters

Influence function-based empirical likelihood for inference of quantile medical costs with censored data

2019 ◽  
Vol 29 (7) ◽  
pp. 1913-1934
Author(s):  
Jenny Jeyarajah ◽  
Guanhao Wei ◽  
Gengsheng Qin
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Empirical Likelihood ◽  
Influence Function ◽  
Likelihood Ratio Statistic ◽  
Medical Costs ◽  
Finite Sample ◽  
Chi Square ◽  
Coverage Probabilities ◽  
Interval Lengths

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example.

scholarly journals Analysis of Nonlinear Regression Models: A Cautionary Note

Dose-Response ◽  
2005 ◽  
Vol 3 (3) ◽  
pp. dose-response.0 ◽  
Author(s):  
Shyamal D. Peddada ◽  
Joseph K. Haseman
Keyword(s):  
Confidence Intervals ◽  
Regression Models ◽  
Nonlinear Models ◽  
Nonlinear Function ◽  
Standard Errors ◽  
True Parameter ◽  
Nonlinear Regression Models ◽  
Confidence Levels ◽  
Regression Parameters ◽  
Large Sample Theory

Regression models are routinely used in many applied sciences for describing the relationship between a response variable and an independent variable. Statistical inferences on the regression parameters are often performed using the maximum likelihood estimators (MLE). In the case of nonlinear models the standard errors of MLE are often obtained by linearizing the nonlinear function around the true parameter and by appealing to large sample theory. In this article we demonstrate, through computer simulations, that the resulting asymptotic Wald confidence intervals cannot be trusted to achieve the desired confidence levels. Sometimes they could underestimate the true nominal level and are thus liberal. Hence one needs to be cautious in using the usual linearized standard errors of MLE and the associated confidence intervals.

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