1
1735-1294
Statistical Research and Training Center - Statistical Centre of Iran
269
General
Prediction of Times to Failure of Censored Units in Progressive Hybrid Censored Samples for the Proportional Hazards Family
Ameli
Samaneh
Rezaie
Majid
Ahmadi
Jafar
1
3
2018
14
2
131
155
27
02
2016
25
11
2017
In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor ($BUP$), the maximum likelihood predictor ($MLP$) and conditional median predictor ($CMP$) are all derived. As an example, the obtained results are computed for exponential distribution. A numerical example is presented to illustrate the prediction methods discussed here. Using simulation studies, the predictors are compared in terms of bias and mean squared prediction error ($MSPE$).
273
General
Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Hosseini Shojaei
Seyed Reza
Waghei
Yadollah
Mohammadzadeh
Mohsen
1
3
2018
14
2
157
169
05
08
2016
16
10
2017
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that random effects have Gaussian distribution, but the assumption is questionable. This assumption is replaced in the present work, using a skew Gaussian distribution for the latent variables, which is more flexible and includes Gaussian distribution. We examine the proposed method using a real discrete data set.
268
General
Rayleigh Confidence Regions based on Record Data
Abdi
Mousa
Asgharzadeh
Akbar
1
3
2018
14
2
171
188
08
08
2016
16
10
2017
This paper presents exact joint confidence regions for the parameters of the Rayleigh distribution based on record data. By providing some appropriate pivotal quantities, we construct several joint confidence regions for the Rayleigh parameters. These joint confidence regions are useful for constructing confidence regions for functions of the unknown parameters. Applications of the joint confidence regions using two environmental data sets are presented for illustrative purposes. Finally, a simulation study is conducted to study the performance of the proposed joint confidence regions.
270
General
Inference for the Type-II Generalized Logistic Distribution with Progressive Hybrid Censoring
Azizpour
Mina
^{
j
}
Asgharzadeh
Akbar
^{
j
}and Akbar Asgharzadeh
1
3
2018
14
2
189
217
31
08
2016
13
09
2017
This article presents the analysis of the Type-II hybrid progressively censored data when the lifetime distributions of the items follow Type-II generalized logistic distribution. Maximum likelihood estimators (MLEs) are investigated for estimating the location and scale parameters. It is observed that the MLEs can not be obtained in explicit forms. We provide the approximate maximum likelihood estimators (AMLEs) by appropriately approximating the likelihood equations. Asymptotic confidence intervals based on MLEs and AMLEs and one bootstrap confidence interval are proposed.
Estimation of the shape parameter is also discussed. Monte Carlo simulations are performed to compare the performances of the different methods and two real data sets have been analyzed for illustrative purposes.
272
General
A Perturbed Half-normal Distribution and Its Applications
Mahmoudi
Eisa
Lalehzari
Reihaneh
Meshkat
Rahmat Sadat
1
3
2018
14
2
219
246
23
12
2016
21
01
2018
In this paper, a new generalization of the half-normal distribution which is called the perturbed half-normal distribution is introduced. The new distribution belongs to a family of distributions which includes the half-normal distribution along with an extra parameter to regulate skewness. The probability density function (pdf) is derived and some various properties of the new distribution are obtained. The derived properties include the cumulative distribution function (cdf), the $r$th moment, moment generating function, characteristic function, mean deviation about the mean and estimation of the parameters using the method of moments and maximum likelihood. Finally, the flexibility and potentiality of the new distribution is illustratedin an application to two real data sets.
271
General
â€‹Rank based Least-squares Independent Component Analysis
Rahmani Shamsi
Jafar
Dolati
Ali
1
3
2018
14
2
247
266
22
02
2017
07
02
2018
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of the proposed algorithm through simulation and real data analysis. Since the proposed algorithm uses rank values rather than the actual values of the observations, it is extremely robust to the outliers and suffers less from the presence of noise than the other algorithms.