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Showing 3 results for Chinipardaz
Razieh Dehghanian, Rahim Chinipardaz, Behzad Mansouri,
Volume 18, Issue 2 (3-2014)
Abstract
Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than linear and quadratic methods because of its flexibility. However, the kernel estimates are depend on the bandwidth. This paper is concerned with the selection of bandwidth parameter to achieve an optimal discrimination with minimum rate misclassification. The methods obtained bandwidth examined via a simulation study.
Dr Rahim Chinipardaz, Dr Behzad Mansouri,
Volume 25, Issue 2 (3-2021)
Abstract
There are two reasons that 2013 named as Statistics year. First, it was 300 year after written the book, Ars Conjectandi, by Bernoulli and the second, presentation of Bayes article 250 year ago. Hald (2007) beleive that the development period of Probability and Statistics is started from Bernoulli and ended by Fisher. This article expaline the role of Bernoulli book in Statistics.
Dr. Behzad Mansouri, Dr. Rahim Chinipardaz, Sami Atiyah Sayyid Al-Farttosi, Dr. Habiballah Habiballah,
Volume 27, Issue 1 (3-2023)
Abstract
The empirical distribution function is used as an estimate of the cumulative probability distribution function
of a random variable. The empirical distribution function has a fundamental role in many statistical inferences, which are
little known in some cases. In this article, the empirical probability function is introduced as a derivative of the empirical
distribution function, and it is shown that moment estimators such as sample mean, sample median, sample variance, and
sample correlation coefficient result from replacing the random variable density function with the empirical probability
function in the theoretical definitions. In addition, the kernel probability density function estimator is used to estimate the
population parameters and a new method for bandwidth estimation in the kernel density estimation is introduced.
Keywords: Empirical distribution function, moment estimate, kernel estimator, bandwidth.
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