Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Testing Skew-Laplace Distribution Using Density-based Empirical Likelihood Approach
1
24
FA
M.
Safavinejad
m_safavinejad1368@yahoo.com
S.
Jomhoori
sjomhoori@birjand.ac.ir
H.
Alizadeh Noughabi
alizadehhadi@birjand.ac.ir
10.18869/acadpub.jsri.13.1.1
Abstract: In this paper, we first describe the skew-Laplace distribution and its properties. We then introduce a goodness of fit test for this distribution according to the density-based empirical likelihood ratio concept. Asymptotic consistency of the proposed test is demonstrated.
The critical values and Type I error of the test are obtained by Monte Carlo simulations.
Moreover, the empirical distribution function (EDF) tests are considered for the skew-Laplace distribution to show they do not have acceptable Type I error in comparison with the proposed test. Results show that the proposed test has an excellent Type I error which does not depend on the unknown parameters.
The results obtained from simulation studies designed to investigate the power of the test are presented, too. The applicability of the proposed test in practice is demonstrated by real data examples.
Density-based empirical likelihood, likelihood ratio, skew-Laplace distribution, goodness of fit tests, Type I error.
http://jsri.srtc.ac.ir/article-1-214-en.html
http://jsri.srtc.ac.ir/article-1-214-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Tsallis Entropy Properties of Order Statistics and Some Stochastic Comparisons
25
41
FA
Simindokht
Baratpour
baratpour@um.ac.ir
Amirhamzeh
Khammar
amirhamzeh.kh@gmail.com
10.18869/acadpub.jsri.13.1.2
Abstract: Tsallis entropy and order statistics are important in engineering reliability, image and signal processing. In this paper, we try to extend the concept of Tsallis entropy using order statistics. For this purpose, we propose the Tsallis entropy of order statistics and for it we obtain upper and lower bounds and some results on stochastic comparisons.
Hazard rate function, order statistics, stochastic comparisons, tsallis entropy.
http://jsri.srtc.ac.ir/article-1-215-en.html
http://jsri.srtc.ac.ir/article-1-215-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Preliminary Test and Shrinkage Estimations of Scale Parameters for Two Exponential Distributions based on Record Values
43
58
FA
Hojatollah
Zakerzadeh
hzaker@yazd.ac.ir
Akbar
Jafari
aajafari@yazd.ac.ir
Mahdieh
Karimi
mkarimiz68@yahoo.com
10.18869/acadpub.jsri.13.1.3
Abstract: The exponential distribution is applied in a very wide variety of statistical procedures. Among the most prominent applications are those in the field of life testing and reliability theory. When there are two record samples available for estimating the scale parameter, a preliminary test is usually used to determine whether to pool the samples or use the individual sample. In this paper, the preliminary test estimator and shrinkage estimator are studied. The optimum level of significance for preliminary test estimation and the optimum values of shrinkage coefficient are obtained based on minimax regret criterion under the weighted square error loss function.
Mean square error, minimax regret criterion, optimal significance level, preliminary test estimation, record values, shrinkage estimator.
http://jsri.srtc.ac.ir/article-1-216-en.html
http://jsri.srtc.ac.ir/article-1-216-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Admissible Set of Rival Models based on the Mixture of Kullback-Leibler Risks
59
88
FA
Abdolreza
Sayyareh
asayyareh@kntu.ac.ir
10.18869/acadpub.jsri.13.1.4
Abstract: Model selection aims to find the optimum model. A good model will generally yield good results. Herein lies the importance of model evaluation criteria for assessing the goodness of a subjective model. In this work we want to answer to this question that, how could infinite set of all possible models that could have given rise to data, be narrowed down to a reasonable set of statistical models? This paper considers a finite mixture of the known criterion to the model selection problem to answer to the question. The aim of this kind of criteria is to select an reasonable set of models based on a measure of closeness. We demonstrate that a very general class of statistical criterion, which we call that finite mixture Kullback-Leibler criterion, provides a way of rival theory model selection. In this work we have proposed two types of coefficients for the mixture criterion, one based on the density and another one based on the risk function. The simulation study and real data analysis confirme the proposed criteria.
Akaike information criterion, Kullback-Leibler divergence, non-nested model selection.
http://jsri.srtc.ac.ir/article-1-217-en.html
http://jsri.srtc.ac.ir/article-1-217-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Stress-strength Reliability of Exponential Distribution based on Type-I Progressively Hybrid Censored Samples
89
105
FA
M.
Mirjalili
mirjalili8@yahoo.com
H.
Torabi
htorabi@yazd.ac.ir
H.
Nadeb
honadeb@yahoo.com
S.
Bafekri. F.
s.bafekri@gmail.com
10.18869/acadpub.jsri.13.1.5
Abstract: This paper considers the estimation of the stress-strength parameter, say R, based on two independent Type-I progressively hybrid censored samples from exponential populations with different parameters. The maximum likelihood estimator and asymptotic confidence interval for R are obtained. Bayes estimator of R is also derived under the assumption of independent gamma priors. A Monte Carlo simulation study is used to evaluate the performance of maximum likelihood estimator, Bayes estimator and asymptotic confidence interval. Finally, a pair of real data sets is analyzed for illustrative purposes.
Stress-strength model, Type-I progressive Hybrid censoring, Bayes estimator, Maximum likelihood estimator, Monte Carlo simulation.
http://jsri.srtc.ac.ir/article-1-218-en.html
http://jsri.srtc.ac.ir/article-1-218-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
1
2016
9
1
Bayesian Method for Finding True Change Point when a Control Chart is used
107
117
FA
Esmail
Dehghan Monfared
monfared2@yahoo.com
Fazlollah
Lak
fazlollahlak@gmail.com
10.18869/acadpub.jsri.13.1.6
Abstract: The process personnel always seek the opportunity to improve the processes. One of the essential steps for process improvement is to quickly find the starting time or the change point of a process disturbance. To do this, after a control chart triggers an out-of-control signal, an order of points in time (known as a plan) should be identified such that if the process examined sequentially at them, the true change point is detected as soon as possible. A typical method is to start the examination of the process from the signal time of the control chart and proceed to neighbouring points. In this paper, we establish a Bayesian method to solve this problem, i.e. to find a plan for examining the process sequentially such that it minimizes the Bayes risk among all other possible plans. At last, our proposed Bayes method is applied to a normal process, and compared to a typical method which is usually used to find the true change point through a series of simulations.
Bayesian method, Bayesian risk, change point, control chart.
http://jsri.srtc.ac.ir/article-1-219-en.html
http://jsri.srtc.ac.ir/article-1-219-en.pdf