Experimental design with short-tailed and long-tailed symmetric error distributions

Download

index.pdf

Date

2004

Author

Yilmaz, Yıldız Elif

Metadata

Show full item record

Item Usage Stats

181
views

73
downloads

One-way and two-way classification models in experimental design for both balanced and unbalanced cases are considered when the errors have Generalized Secant Hyperbolic distribution. Efficient and robust estimators for main and interaction effects are obtained by using the modified maximum likelihood estimation (MML) technique. The test statistics analogous to the normal-theory F statistics are defined to test main and interaction effects and a test statistic for testing linear contrasts is defined. It is shown that test statistics based on MML estimators are efficient and robust. The methodogy obtained is also generalized to situations where the error distributions from block to block are non-identical.

Subject Keywords

Probabilities.

URI

http://etd.lib.metu.edu.tr/upload/12605191/index.pdf
https://hdl.handle.net/11511/14346

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.
Analysis of stochastic and non-stochastic volatility models. Özkan, Pelin; Ayhan, Hüseyin Öztaş; Department of Statistics (2004) Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order...
Comparison of regression techniques via Monte Carlo simulation Mutan, Oya Can; Ayhan, Hüseyin Öztaş; Department of Statistics (2004) The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolut...
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.

Citation Formats

Y. E. Yilmaz, “Experimental design with short-tailed and long-tailed symmetric error distributions,” M.S. - Master of Science, Middle East Technical University, 2004.