Journal of the Iranian Statistical Society
http://jirss@irstat.ir
Journal of The Iranian Statistical Society - Journal articles for year 2011, Volume 10, Number 1Yektaweb Collection - http://www.yektaweb.comen2011/3/10Statistical Evidences in Type-II Censored Data
http://jirss.irstat.ir/browse.php?a_id=117&sid=1&slc_lang=en
In this article, we use a measure of expected true evidence
for determine the required sample size in type-II censored experiments
for obtaining statistical evidence in favor of one hypothesis about the
exponential mean against another.
A. Habibi RadPower Function Distribution Characterized by Dual Generalized Order Statistics
http://jirss.irstat.ir/browse.php?a_id=118&sid=1&slc_lang=en
The dual generalized order statistics is a unified model which
contains the well known decreasingly ordered random data such as (reversed
ordered) order statistics and lower record values. In the present
paper, some characterization results on the power function distribution
based on the properties of dual generalized order statistics are provided.
The results are proved without any restriction on the parameters of the
model of dual GOS.
Mahdi TavangarStochastic and Dependence Comparisons Between Extreme Order Statistics in the Case of Proportional Reversed Hazard Model
http://jirss.irstat.ir/browse.php?a_id=119&sid=1&slc_lang=en
Independent random
variables $Y_{1},ldots ,Y_{n}$ belongs to the
proportional reversed hazard rate (PRHR) model with
proportionality parameters $lambda_1,...,lambda_n$, if
$Y_{k}sim G^{lambda _{k}}(x)$, for $k=1,...,n$, where $G$ is an
absolutely continuous distribution function. In this paper we compare
the smallest order
statistics, the sample ranges and the ratios of the smallest and
largest order statistics of two sets of independent random
variables belonging to PRHR model, in the sense of (reversed) hazard
rate order, likelihood ratio order and dispersive order, when the
variables in one set have proportionality parameters
$lambda_1,...,lambda_n$ and the variables in the other set are
independent and identically distributed with common parameter
$overline{lambda}=sum_{k=1}^{n}lambda_k/n$. We also compare
the relative degree of dependence between the smallest and the largest
order statistics of these samples whit respect to the monotone
regression dependence order.
Ali DolatiDensity Estimators for Truncated Dependent Data
http://jirss.irstat.ir/browse.php?a_id=120&sid=1&slc_lang=en
In some long term studies, a series of dependent and possibly
truncated lifetime data may be observed. Suppose that the lifetimes
have a common continuous distribution function F. A popular stochastic
measure of the distance between the density function f of the lifetimes
and its kernel estimate fn is the integrated square error (ISE). In this
paper, we derive a central limit theorem for the integrated square error
of the kernel density estimators in the left-truncation model. It is
assumed that the lifetime observations form a stationary strong mixing
sequence. A central limit theorem (CLT) for the ISE of the kernel hazard
rate estimators is also presented.
V. FakoorBayesian Two-Sample Prediction with Progressively Type-II Censored Data for Some Lifetime Models
http://jirss.irstat.ir/browse.php?a_id=121&sid=1&slc_lang=en
Prediction on the basis of censored data is very important
topic in many fields including medical and engineering sciences. In this
paper, based on progressive Type-II right censoring scheme, we will discuss
Bayesian two-sample prediction. A general form for lifetime model
including some well known and useful models such asWeibull and Pareto
is considered for obtaining prediction bounds as well as Bayes predictive
estimations under squared error loss function for the sth order statistic
in a future random sample drawn from the parent population, independently
and with an arbitrary progressive censoring scheme. As an
illustration, we will present two numerical examples as well as a simulation
study to carry out the performance of the procedures obtained.
S. Ghafoori