Robust estimation and hypothesis testing under short-tailedness and inliers

Date

2005-06-01

Author

Akkaya, Ayşen

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

251
views

0
downloads

Estimation and hypothesis testing based on normal samples censored in the middle are developed and shown to be remarkably efficient and robust to symmetric short-tailed distributions and to inliers in a sample. This negates the perception that sample mean and variance are the best robust estimators in such situations (Tiku, 1980; Dunnett, 1982).

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability

URI

https://hdl.handle.net/11511/34867

Journal

TEST

DOI

https://doi.org/10.1007/bf02595400

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Analysis of Covariance with Non-normal Errors ŞENOĞLU, BİRDAL; Avcioglu, Mubeccel Didem (Wiley, 2009-12-01) P>Analysis of covariance techniques have been developed primarily for normally distributed errors. We give solutions when the errors have non-normal distributions. We show that our solutions are efficient and robust. We provide a real-life example.
Linear contrasts in experimental design with non-identical error distributions Senoglu, B; Tiku, ML (Wiley, 2002-01-01) Estimation of linear contrasts in experimental design, and testing their assumed values, is considered when the error distributions from block to block are not necessarily identical. The normal-theory solutions are shown to have low efficiencies as compared to the solutions presented here.
A marginalized multilevel model for bivariate longitudinal binary data Inan, Gul; İlk Dağ, Özlem (Springer Science and Business Media LLC, 2019-06-01) This study considers analysis of bivariate longitudinal binary data. We propose a model based on marginalized multilevel model framework. The proposed model consists of two levels such that the first level associates the marginal mean of responses with covariates through a logistic regression model and the second level includes subject/time specific random intercepts within a probit regression model. The covariance matrix of multiple correlated time-specific random intercepts for each subject is assumed to ...
Estimation and hypothesis testing in multivariate linear regression models under non normality İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01) This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Autoregressive models with short-tailed symmetric distributions Akkaya, Ayşen (Informa UK Limited, 2008-01-01) Symmetric short-tailed distributions do indeed occur in practice but have not received much attention particularly in the context of autoregression. We consider a family of such distributions and derive the modified maximum likelihood estimators of the parameters. We show that the estimators are efficient and robust. We develop hypothesis-testing procedures.

Citation Formats

A. Akkaya, “Robust estimation and hypothesis testing under short-tailedness and inliers,” TEST, pp. 129–150, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34867.