Pub Date : 2020-10-01DOI: 10.2991/jsta.d.201006.001
I. Moffat, James Augustine Ukpabio, Emmanuel Alphonsus Akpan
The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains. The traditional Markov chain and logarithmic-modified Markov chain were considered. On exploring the yearly data on the stock prices from 2015 to 2018 as obtained from the Nigerian Stock Exchange, it was found that the steady state of logarithmic-modified Markov chain converged faster than the tradition Markov chain with efficiency in tracking the correct cycles where the stock movements are trending irrespective of which cycle it starts at time zero with differences in probability values by 1.1%, 0.7%, −0.41% and −1.37% for accumulation, markup, distribution and mark-down cycles, respectively. Thus, it could be deduced that the logarithmic modification enhances the ability of the Markov chain to tract the variation of the steady state probabilities faster than the traditional counterpart.
{"title":"Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach","authors":"I. Moffat, James Augustine Ukpabio, Emmanuel Alphonsus Akpan","doi":"10.2991/jsta.d.201006.001","DOIUrl":"https://doi.org/10.2991/jsta.d.201006.001","url":null,"abstract":"The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains. The traditional Markov chain and logarithmic-modified Markov chain were considered. On exploring the yearly data on the stock prices from 2015 to 2018 as obtained from the Nigerian Stock Exchange, it was found that the steady state of logarithmic-modified Markov chain converged faster than the tradition Markov chain with efficiency in tracking the correct cycles where the stock movements are trending irrespective of which cycle it starts at time zero with differences in probability values by 1.1%, 0.7%, −0.41% and −1.37% for accumulation, markup, distribution and mark-down cycles, respectively. Thus, it could be deduced that the logarithmic modification enhances the ability of the Markov chain to tract the variation of the steady state probabilities faster than the traditional counterpart.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"6 1","pages":"439-445"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83454440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.2991/jsta.d.200923.001
Sung Ik Kim, Y. S. Kim
In this paper, we introduce a fractional Generalized Hyperbolic process, a new stochastic process with long-range dependence obtained by subordinating fractional Brownianmotion to a fractionalGeneralized InverseGaussian process. The basic properties and covariance structure between the elements of the processes are discussed, and we present numerical methods to generate the sample paths for the processes.
{"title":"A New Stochastic Process with Long-Range Dependence","authors":"Sung Ik Kim, Y. S. Kim","doi":"10.2991/jsta.d.200923.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200923.001","url":null,"abstract":"In this paper, we introduce a fractional Generalized Hyperbolic process, a new stochastic process with long-range dependence obtained by subordinating fractional Brownianmotion to a fractionalGeneralized InverseGaussian process. The basic properties and covariance structure between the elements of the processes are discussed, and we present numerical methods to generate the sample paths for the processes.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"88 1","pages":"432-438"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81418892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.2991/jsta.d.201016.003
Muhammad Shuaib Khan, R. King, I. Hudson
This paper investigates the potential usefulness of the transmuted Kumaraswamy Weibull distribution by using quadratic rank transmutationmap technique formodelling reliability data. Some structural properties of the transmutedKumaraswamyWeibull distribution are discussed. We propose a location-scale regression model based on the transmuted log-Kumaraswamy Weibull distribution for modelling survival data. We discuss estimation of the model parameters by the method of maximum likelihood and provide two applications to illustrate the potentiality of the proposed family of lifetime distributions.
{"title":"Transmuted Kumaraswamy Weibull Distribution with Covariates Regression Modelling to Analyze Reliability Data","authors":"Muhammad Shuaib Khan, R. King, I. Hudson","doi":"10.2991/jsta.d.201016.003","DOIUrl":"https://doi.org/10.2991/jsta.d.201016.003","url":null,"abstract":"This paper investigates the potential usefulness of the transmuted Kumaraswamy Weibull distribution by using quadratic rank transmutationmap technique formodelling reliability data. Some structural properties of the transmutedKumaraswamyWeibull distribution are discussed. We propose a location-scale regression model based on the transmuted log-Kumaraswamy Weibull distribution for modelling survival data. We discuss estimation of the model parameters by the method of maximum likelihood and provide two applications to illustrate the potentiality of the proposed family of lifetime distributions.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74730444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.2991/jsta.d.201016.002
E. Nkemnole, Emmanuel M. Ikegwu
{"title":"Poly-Weighted Exponentiated Gamma Distribution with Application","authors":"E. Nkemnole, Emmanuel M. Ikegwu","doi":"10.2991/jsta.d.201016.002","DOIUrl":"https://doi.org/10.2991/jsta.d.201016.002","url":null,"abstract":"","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"26 2 1","pages":"446-459"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77990980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200917.002
E. Mahmoudi, R. Meshkat
This paper introduces a special case of weightedk-out-ofn:G system formed from two types of nonidentical components with different weights. This system consists of n nonidentical components each with its own positive integer-valued weight which are categorized into two groups with respect to their duties and services. In fact, we have a system consisting n components such that n1 of them each with its own weight ωi and reliability p1i and n2 of them each with its own weight ω∗ i and reliability p2i. If the total weights of the functioning components exceeds a prespecified threshold k, the system is supposed to work. The reliability of system is obtained based on the total weight of all working components in both group. The survival function and mean time to failure are presented. Also, the component importance of this system are studied.
{"title":"Reliability Analysis of Weighted- k-out-of- n: G System Consisting of Two Different Types of Nonidentical Components Each with its Own Positive Integer-Valued Weight","authors":"E. Mahmoudi, R. Meshkat","doi":"10.2991/jsta.d.200917.002","DOIUrl":"https://doi.org/10.2991/jsta.d.200917.002","url":null,"abstract":"This paper introduces a special case of weightedk-out-ofn:G system formed from two types of nonidentical components with different weights. This system consists of n nonidentical components each with its own positive integer-valued weight which are categorized into two groups with respect to their duties and services. In fact, we have a system consisting n components such that n1 of them each with its own weight ωi and reliability p1i and n2 of them each with its own weight ω∗ i and reliability p2i. If the total weights of the functioning components exceeds a prespecified threshold k, the system is supposed to work. The reliability of system is obtained based on the total weight of all working components in both group. The survival function and mean time to failure are presented. Also, the component importance of this system are studied.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"44 1","pages":"408-414"},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83023722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200917.001
E. Mahmoudi, Ameneh Rostami
In this paper, we introduce a first-order nonnegative integer-valuedmoving average process with power series innovations based on a Poisson thinning operator (PINMAPS(1)) formodeling overdispersed, equidispersed and underdispersed count time series. This process contains the PINMA process with geometric, Bernoulli, Poisson, binomial, negative binomial and logarithmic innovations which some of them are studied in details. Some statistical properties of the process are obtained. The unknown parameters of the model are estimated using the Yule-Walker, conditional least squares and least squares feasible generalized methods. Also, the performance of estimators is evaluated using a simulation study. Finally, we apply the model to three real data set and show the ability of the model for predicting data compared to competing models.
{"title":"First-Order Integer-Valued Moving Average Process with Power Series Innovations","authors":"E. Mahmoudi, Ameneh Rostami","doi":"10.2991/jsta.d.200917.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200917.001","url":null,"abstract":"In this paper, we introduce a first-order nonnegative integer-valuedmoving average process with power series innovations based on a Poisson thinning operator (PINMAPS(1)) formodeling overdispersed, equidispersed and underdispersed count time series. This process contains the PINMA process with geometric, Bernoulli, Poisson, binomial, negative binomial and logarithmic innovations which some of them are studied in details. Some statistical properties of the process are obtained. The unknown parameters of the model are estimated using the Yule-Walker, conditional least squares and least squares feasible generalized methods. Also, the performance of estimators is evaluated using a simulation study. Finally, we apply the model to three real data set and show the ability of the model for predicting data compared to competing models.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"85 1","pages":"415-431"},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81595113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200824.001
O. Kubaychuk
Themodel of amixture of several probability distributions wasmentioned for the first time byNewcomb [1] and Pearson [2]. Suchmixtures naturally arise inmany areas. In particular, in the theory of reliability and time of life,mixtures of gammadistributions [3] are used. Examples of the use of mixtures of normal distributions in the processing of biological and physiological data are given in [4]. In Slud [5], a mixture of two exponential distributions is used to describe the debugging process of the software. Some applications of the model of mixtures in medical diagnostics were given in [6,7].
{"title":"EBC-Estimator of Multidimensional Bayesian Threshold in Case of Two Classes","authors":"O. Kubaychuk","doi":"10.2991/jsta.d.200824.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200824.001","url":null,"abstract":"Themodel of amixture of several probability distributions wasmentioned for the first time byNewcomb [1] and Pearson [2]. Suchmixtures naturally arise inmany areas. In particular, in the theory of reliability and time of life,mixtures of gammadistributions [3] are used. Examples of the use of mixtures of normal distributions in the processing of biological and physiological data are given in [4]. In Slud [5], a mixture of two exponential distributions is used to describe the debugging process of the software. Some applications of the model of mixtures in medical diagnostics were given in [6,7].","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"27 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89768819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200827.001
S. Yaghoubi, R. Farnoosh
This paper proposes a flexible finite mixture model framework using multivariate skew normal distribution for banking and credit institutions’ stock data in Iran. This method clusters time series stocks data of Iranian banks and credit institutions to filter those data into four groups. The proposed model estimates matrices of time-varying parameter for skew normal distribution mixture using EM algorithm, updating the estimated parameters via generalized autoregressive score (GAS) model. Empirical studies are conducted to examine the effect of the proposed model in clustering, estimating, and updating parameters for real data from 12 sets of stocks. Our stock data were filtered in four trade clusters with best performance.
{"title":"Model-Based Filtering via Finite Skew Normal Mixture for Stock Data","authors":"S. Yaghoubi, R. Farnoosh","doi":"10.2991/jsta.d.200827.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200827.001","url":null,"abstract":"This paper proposes a flexible finite mixture model framework using multivariate skew normal distribution for banking and credit institutions’ stock data in Iran. This method clusters time series stocks data of Iranian banks and credit institutions to filter those data into four groups. The proposed model estimates matrices of time-varying parameter for skew normal distribution mixture using EM algorithm, updating the estimated parameters via generalized autoregressive score (GAS) model. Empirical studies are conducted to examine the effect of the proposed model in clustering, estimating, and updating parameters for real data from 12 sets of stocks. Our stock data were filtered in four trade clusters with best performance.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"31 1","pages":"391-396"},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74491495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200825.002
D. Kumar, Maneesh Kumar, S. Dey
In this paper, we first derive the exact explicit expressions for the single and product moments of order statistics from the typeII exponentiated log-logistic distribution, and then use these results to compute the means, variances, skewness and kurtosis of rth order statistics. Besides, best linear unbiased estimators (BLUEs) for the location and scale parameters for the type-II exponentiated log-logistic distribution with known shape parameters are studied. Finally, the results are illustrated with a real data set.
{"title":"Inferences for the Type-II Exponentiated Log-Logistic Distribution Based on Order Statistics with Application","authors":"D. Kumar, Maneesh Kumar, S. Dey","doi":"10.2991/jsta.d.200825.002","DOIUrl":"https://doi.org/10.2991/jsta.d.200825.002","url":null,"abstract":"In this paper, we first derive the exact explicit expressions for the single and product moments of order statistics from the typeII exponentiated log-logistic distribution, and then use these results to compute the means, variances, skewness and kurtosis of rth order statistics. Besides, best linear unbiased estimators (BLUEs) for the location and scale parameters for the type-II exponentiated log-logistic distribution with known shape parameters are studied. Finally, the results are illustrated with a real data set.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"54 4","pages":"352-367"},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72450604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.2991/jsta.d.200825.001
N. Abbas
The q-Weibull distribution is a generalized form of the Weibull distribution and has potential to model complex systems and life time datasets. Bayesian inference is the modern statistical technique that can accommodate uncertainty associated with the model parameters in the form of prior distributions. This study presents Bayesian analysis of the q-Weibull distribution using uninformative and informative priors and the results are compared with those produced by the classical maximum likelihood (ML) and least-squares (LS) estimation method. A simulation study is also made to compare the two methods. Different model selection criteria and predicted datasets are considered to compare the inferential methods under study. Posterior analyses include evaluating posterior means, medians, credible intervals of highest density regions, and posterior predictive distributions. The entire analysis is carried out using Markov chain Monte Carlo (MCMC) setup using WinBUGS package. The Bayesian method has proved to be superior to its classical counterparts. A real dataset is used to illustrate the entire inferential procedure.
{"title":"On Examining Complex Systems Using the q-Weibull Distribution in Classical and Bayesian Paradigms","authors":"N. Abbas","doi":"10.2991/jsta.d.200825.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200825.001","url":null,"abstract":"The q-Weibull distribution is a generalized form of the Weibull distribution and has potential to model complex systems and life time datasets. Bayesian inference is the modern statistical technique that can accommodate uncertainty associated with the model parameters in the form of prior distributions. This study presents Bayesian analysis of the q-Weibull distribution using uninformative and informative priors and the results are compared with those produced by the classical maximum likelihood (ML) and least-squares (LS) estimation method. A simulation study is also made to compare the two methods. Different model selection criteria and predicted datasets are considered to compare the inferential methods under study. Posterior analyses include evaluating posterior means, medians, credible intervals of highest density regions, and posterior predictive distributions. The entire analysis is carried out using Markov chain Monte Carlo (MCMC) setup using WinBUGS package. The Bayesian method has proved to be superior to its classical counterparts. A real dataset is used to illustrate the entire inferential procedure.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"18 1","pages":"368-382"},"PeriodicalIF":1.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87370267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：