This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 539
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Quality and Productivity
Abstract - #309209
Title: The Unit Interval Characteristic Distributions and Their Applications
Author(s): Fassil Nebebe*+
Companies: Concordia University
Address: Department Of Decision Sciences & MIS, Montreal, QC, H3G 1M8, Canada
Keywords: bootstrap sampling distributions ; fitting empirical data ; goodness of fit ; prediction intervals
Abstract:

Mak and Nebebe (2009) proposed a very general class of probability distributions, the "unit interval characteristic" (UIC) distributions which includes normal distributions as special cases. A variety of distributions with widely different shapes and supports can be closely approximated by this class of distributions. We consider in this paper the fitting of empirical data with the UIC distributions using both maximum likelihood and goodness of fit measures. In particular, their applications in fitting general regression model with non-normal errors (and possibly heteroscedasticity) and statistical inferences will be examined in details. In addition, computer intensive methods will also be applied using the UIC distributions to approximate the bootstrap sampling distributions, resulting in a substantial reduction of computing costs.


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