Short-tailed distributions and inliers

We consider two families of short-tailed distributions (kurtosis less than 3) and discuss their usefulness in modeling numerous real life data sets. We develop estimation and hypothesis testing procedures which are efficient and robust to short-tailed distributions and inliers.


FGX: a frequentist gene expression index for Affymetrix arrays
Purutçuoğlu Gazi, Vilda (Oxford University Press (OUP), 2007-04-01)
We consider a new frequentist gene expression index for Affymetrix oligonucleotide DNA arrays, using a similar probe intensity model as suggested by Hein and others (2005), called the Bayesian gene expression index (BGX). According to this model, the perfect match and mismatch values are assumed to be correlated as a result of sharing a common gene expression signal. Rather than a Bayesian approach, we develop a maximum likelihood algorithm for estimating the underlying common signal. In this way, estimatio...
Time series AR(1) model for short-tailed distributions
Akkaya, AD; Tiku, ML (Informa UK Limited, 2005-04-01)
The innovations in AR(1) models in time series have primarily been assumed to have a normal or long-tailed distributions. We consider short-tailed distributions (kurtosis less than 3) and derive modified maximum likelihood (MML) estimators. We show that the MML estimator of 0 is considerably more efficient than the commonly used least squares estimator and is also robust. This paper is essentially the first to achieve robustness to inliers and to various forms of short-tailedness in time series analysis.
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.
Regression analysis with a dtochastic design variable
Sazak, HS; Tiku, ML; İslam, Muhammed Qamarul (Wiley, 2006-04-01)
In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
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
A. Akkaya, “Short-tailed distributions and inliers,” TEST, pp. 282–296, 2008, Accessed: 00, 2020. [Online]. Available: