Pairwise multiple comparisons under short-tailed symmetric distribution

Download

index.pdf

Date

2007

Author

Balcı, Sibel

Metadata

Show full item record

Item Usage Stats

290
views

103
downloads

In this thesis, pairwise multiple comparisons and multiple comparisons with a control are studied when the observations have short-tailed symmetric distributions. Under non-normality, the testing procedure is given and Huber estimators, trimmed mean with winsorized standard deviation, modified maximum likelihood estimators and ordinary sample mean and sample variance used in this procedure are reviewed. Finally, robustness properties of the stated estimators are compared with each other and it is shown that the test based on the modified maximum likelihood estimators has better robustness properties under short-tailed symmetric distribution.

Subject Keywords

Statistics.

URI

http://etd.lib.metu.edu.tr/upload/3/12608406/index.pdf
https://hdl.handle.net/11511/16745

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

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...
Multiple linear regression model with stochastic design variables İslam, Muhammed Qamarul (Informa UK Limited, 2010-01-01) In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
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.
Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models Yozgatlıgil, Ceylan (Informa UK Limited, 2009-11-01) Time series often contain observations of several variables and multivariate time series models are used to represent the relationship between these variables. There are many studies on vector autoregressive moving average (VARMA) models, but the representation of multiplicative seasonal VARMA models has not been seriously studied. In a multiplicative vector model, such as a seasonal VARMA model, the representation is not unique because of the noncommutative property of matrix multiplication. In this articl...
Marginalized transition random effect models for multivariate longitudinal binary data İlk Dağ, Özlem (Wiley, 2007-03-01) Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999, 2002) to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within-subject, dependence, either...

Citation Formats

S. Balcı, “Pairwise multiple comparisons under short-tailed symmetric distribution,” M.S. - Master of Science, Middle East Technical University, 2007.