An Application of the Functional Central Limit Theorem in the Unit Root Test in Autoregressive Models
Sedigheh
Zamani Mehrian
دانشجوی دکترا آمار، دانشگاه آمار
author
Alireza
nematollahi
استاد گروه آمار، دانشگاه شیراز
author
text
article
2015
per
To study the limiting distribution of the test statistics used in the unit root problems, we usually need to use the known theorem Donsker (Functional central limit theorem). In this paper, we study the limiting behavior of the unit root test statistics in the AR (1) model without and with a constant term by an indirect use of the Donsker theorem where the error terms are with noise. We also consider the case when the error terms are nonwhite noise stationary and then generalize our results to the AR (p) models. Several examples are provided to clarify the issue. To study the limiting distribution of the test statistics used in the unit root problems, we usually need to use the known theorem Donsker (Functional central limit theorem). In this paper, we study the limiting behavior of the unit root test statistics in the AR (1) model without and with a constant term by an indirect use of the Donsker theorem where the error terms are with noise. We also consider the case when the error terms are nonwhite noise stationary and then generalize our results to the AR (p) models. Several examples are provided to clarify the issue.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
3
9
https://stat.journals.pnu.ac.ir/article_1945_e6998df00879c9870f7e8ec0574ec243.pdf
The Introduction of General Progressive Type II Censoring
Ameneh
Mirniam
دانشجوی دکتری، آمار دانشگاه شیراز
author
Zahra
Shenavari
دانشجوی دکتری، آمار دانشگاه شیراز
author
A.
Borhani Haghighi
استادیار، بخش آمار دانشگاه شیراز
author
text
article
2015
per
In this article, at first the concept of the failure time is expressed and then censoring and its varieties, especially General Progressive Type II Censoring design, as well as its properties are studied in the form of some theorems. Also an example is presented to illustrate the methodology, definitions and properties.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
10
14
https://stat.journals.pnu.ac.ir/article_1946_dfff24b36dacad7154960997fda2e590.pdf
Test Statistic Distribution of Composite Hypothesis with Parameter Space Restriction in Continuous Multivariate Distribution
Abozar
Bazyari
استادیار، گروه آمار دانشگاه خلیج فارس
author
text
article
2015
per
The null hypothesis testing of linear combination of p-dimensional parameter vector associated with an known and full rank matrix against the one sided linear combination of parameter vector for a continuous multivariate distribution is considered. The general form of test statistic is computed by likelihood ratio method. Also, the asymptotic null distribution of test statistic is derived by limit theorems according to the chi-square distribution and critical values of test statistic for different significance levels computed and power of test estimated by using Monte Carlo simulation. The numerical examples associated with the problem of testing are presented. All the results of this paper are for independently and identically distributed random vectors. Also, the results are established for a continuous univariate distribution.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
15
26
https://stat.journals.pnu.ac.ir/article_1947_64f311c1858e2651a121f63139e46e34.pdf
Extension of Farlie – Gumbel -Morgenstern Copulas and its Dependence Structure
Hakim
Bekrizadeh
استادیار، گروه آمار دانشگاه پیام نور مرکز ایلام
author
Gholamali
Parham
دانشیار، گروه آمار دانشگاه شهید چمران اهواز
author
Narges
Abbasi
دانشیار، گروه آمار دانشگاه پیام نور
author
Maryam
Rozdar
دانشجوی کارشناسی ارشد، آمار دانشگاه پیام نور
author
text
article
2015
per
Since the dependency domain is limited, it is not possible to model high dependency variables is not possible in Farlie – Gumbel - Morgenstern copulas. To remove mentioned limitation, an extension of the FGM copula is introduced, which is basically in terms of polynomial section of degree on . Also, we study measurements and dependency concepts in introduced extension.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
27
34
https://stat.journals.pnu.ac.ir/article_1948_a977fa776e06db48fd568a4a69978959.pdf
Some Limiting Results for Randomly Weighted Average
Rasool
roozegar
استادیار گروه آمار، دانشگاه یزد
author
E
mahmoudi
دانشیار گروه آمار، دانشگاه یزد
author
text
article
2015
per
Randomly weighted average (RWA) is a suitable alternative to the sample mean in estimating unknown parameter in population, especially when the random weights are unequal. Establishing good limiting results for a sequence of random variables is one of the most important features in theoretical, applied probability and statistical inference. In this paper for randomly weighted average, some limiting results specially weak and strong law of large numbers is obtained. Central limit theorem for randomly weighted average in some special cases and generally is investigated.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
34
42
https://stat.journals.pnu.ac.ir/article_1949_78ec2504c57a617cabe1f5d74cc7ab6b.pdf
Mean Squared Error Comparison of Varianceshrinkage Estimators with Known coefficient of Variation in Skew Normal Distribution
Nahid
Sanjari Farsipoor
استاد، گروه آمار، دانشگاه الزهرا
author
Najmeh
Rashidi
گروه آمار، دانشگاه الزهرا
author
Arash
Pishdast
گروه آمار، دانشگاه علامه طباطبایی
author
text
article
2015
per
Estimating the parameters of population was considered by various statisticians, which this may be occured when the coefficient of variation, skewness or kurtosis (i.e. prior information) was known. Recently Laheetharan and wijekoon (2008) considered an extended method for obtaining optimal shrinkage estimators.Based on the theorems in Laheetharan and wijekoon (2008) we want to obtain optimum shrinkage estimators for mean and variance parameters in skew normal distribution and using MSE criterion we produce estimators for the variance of skew normal distribution, and using MSE criterion we compare these two variance estimators.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
43
48
https://stat.journals.pnu.ac.ir/article_1950_bf935d4e35d4162efe47cc594a73cd1e.pdf
Robust Generalized Estimating Equations Method and its Application in Correlated Binary Outcomes Models
Masood
Yarmohammadi
دانشیار، گروه آمار دانشگاه پیام نور
author
Saeid
Madani
دانشجوی دکترا، آمار دانشگاه پیام نور
author
text
article
2015
per
The generalized estimating equations method of Liang and Zeger (1986) facilitates analysis of data collected in longitudinal, nested, or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression when there is unkown correlation among the observations. This method can be highly influenced by the presence of outliers and looze its efficiency. To reduce the effects of these data a robustified generalized estimating equations for Schweppe and Mallos classes are introduced. Then we will compare these procedures with the unrobused GEE by simulation studies for correlated binary outcomes models.
دوفصلنامه گستره علوم آماری
دانشگاه پیام نور
2476-3632
1
v.
1 (پاییز و زمستان 1394)
no.
2015
49
59
https://stat.journals.pnu.ac.ir/article_1951_022227ece6ead10bcaac7db22f1b1e86.pdf