Confidence Intervals Based on Robust Estimators

Confidence Intervals Based on Robust Estimators

Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators

Maximum pseudolikelihood estimation (MPLE) constitutes a computationally efficient and easily implemented alternative to maximum likelihood and simulationbased methods. The MPLE has been shown to be consistent and asymptotically normally distributed in a number of interesting cases. However, the coverage probability of the conventional confidence interval for the MPLE is biased downward. We pro...

Robust misinterpretation of confidence intervals.

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless...

Confidence Intervals for Lower Quantiles Based on Two-Sample Scheme

In this paper, a new two-sampling scheme is proposed to construct appropriate confidence intervals for the lower population quantiles. The confidence intervals are determined in the parametric and nonparametric set up and the optimality problem is discussed in each case. Finally, the proposed procedure is illustrated via a real data set. 

Confidence intervals for the largest root of autoregressive models based on instrumental variable estimators

For estimating the largest root of autoregressive (AR) models, we propose an instrumental variable scheme which discounts a large value of regressors corresponding to the largest roots. The pivotal value of the estimator of the largest root is asymptotically normal for any value of the largest root. This fact allows us to construct a simple confidence interval based on 6standard error, say, wit...