Pub Date : 2016-08-25DOI: 10.18869/ACADPUB.JIRSS.15.2.1
Y. Shiferaw, J. Galpin
Small area estimates have received much attention from both private and public sectors due to the growing demand for effective planning of health services, apportioning of government funds and policy and decision making. Surveys are generally designed to give representative estimates at national or district level, but estimates of variables of interest are often also needed at lower levels. These cannot be reliably obtained from the survey data as the sample sizes at these levels are too small. Census data are often available, but only give limited information with respect to the variables of interest. This problem is addressed by using small area estimation techniques, which combine the estimates from the survey and census data sets. The main purpose of this paper is obtaining confidence intervals based on the empirical best linear unbiased predictor (EBLUP) estimates. One of the criticism of the mean squared error (MSE) estimators is that it is not area-specific since it does not involve the direct estimator in its expression. However, most of the confidence intervals in the literature are constructed based on those MSEs. In this paper, we propose area specific confidence intervals for small area parameters under the Fay-Herriot model using area specific MSEs. We extend these confidence intervals to the difference between two small area means. The effectiveness of the proposed methods are also investigated via simulation studies and compared with the Cox (1975) and Prasad and Prasad and Rao (1990) methods. Our simulation results show that the proposed methods have higher coverage probabilities. Those methods are applied to the percentage of food expenditure measures in Ethiopia using the 2010/11 Household Consumption Expenditure (HCE) survey and the 2007 census data sets. Corresponding Author: Yegnanew A Shiferaw (yegnanews@uj.ac.za) Jacqueline S Galpin (Jacqueline.Galpin@wits.ac.za). D ow nl oa de d fr om ji rs s. irs ta t.i r at 0 :5 4 + 03 30 o n T hu rs da y D ec em be r 6t h 20 18 [ D O I: 10 .1 88 69 /a ca dp ub .ji rs s. 15 .2 .1 ] 2 Shiferaw and Galpin
由于对保健服务的有效规划、政府资金的分配以及政策和决策的需求日益增加，小地区估计受到私营和公共部门的高度重视。调查的目的一般是在国家或地区一级作出有代表性的估计，但在较低一级也往往需要对有关变数作出估计。由于这些水平的样本量太小，因此无法从调查数据中可靠地获得这些数据。人口普查数据经常可用，但只提供有关感兴趣的变量的有限信息。这个问题是通过使用小区域估计技术来解决的，该技术结合了调查和普查数据集的估计。本文的主要目的是基于经验最佳线性无偏预测器(EBLUP)估计获得置信区间。对均方误差(MSE)估计量的批评之一是，它不是特定于区域的，因为它在其表达式中不涉及直接估计量。然而，文献中的大多数置信区间都是基于这些mse构建的。在本文中，我们提出了Fay-Herriot模型下的小区域参数的区域特定置信区间。我们将这些置信区间扩展到两个小面积均值之间的差。所提出方法的有效性也通过模拟研究进行了调查，并与Cox(1975)、Prasad和Prasad and Rao(1990)的方法进行了比较。仿真结果表明，该方法具有较高的覆盖概率。这些方法采用2010/11年度家庭消费支出(HCE)调查和2007年人口普查数据集，应用于埃塞俄比亚的食品支出百分比措施。通讯作者:Yegnanew A Shiferaw (yegnanews@uj.ac.za); Jacqueline S Galpin (Jacqueline.Galpin@wits.ac.za)。噢问oa de D fr om霁rs美国国税局ta t.i r (0: 5 4 + 03 30 o n T胡rs da y D ec em r 6 T h 20 18 [D o我:10。1 88 69 / ca dp乌兰巴托济南rs。15。2。1)2 Shiferaw和Galpin
{"title":"Area specific confidence intervals for a small area mean under the Fay-Herriot model","authors":"Y. Shiferaw, J. Galpin","doi":"10.18869/ACADPUB.JIRSS.15.2.1","DOIUrl":"https://doi.org/10.18869/ACADPUB.JIRSS.15.2.1","url":null,"abstract":"Small area estimates have received much attention from both private and public sectors due to the growing demand for effective planning of health services, apportioning of government funds and policy and decision making. Surveys are generally designed to give representative estimates at national or district level, but estimates of variables of interest are often also needed at lower levels. These cannot be reliably obtained from the survey data as the sample sizes at these levels are too small. Census data are often available, but only give limited information with respect to the variables of interest. This problem is addressed by using small area estimation techniques, which combine the estimates from the survey and census data sets. The main purpose of this paper is obtaining confidence intervals based on the empirical best linear unbiased predictor (EBLUP) estimates. One of the criticism of the mean squared error (MSE) estimators is that it is not area-specific since it does not involve the direct estimator in its expression. However, most of the confidence intervals in the literature are constructed based on those MSEs. In this paper, we propose area specific confidence intervals for small area parameters under the Fay-Herriot model using area specific MSEs. We extend these confidence intervals to the difference between two small area means. The effectiveness of the proposed methods are also investigated via simulation studies and compared with the Cox (1975) and Prasad and Prasad and Rao (1990) methods. Our simulation results show that the proposed methods have higher coverage probabilities. Those methods are applied to the percentage of food expenditure measures in Ethiopia using the 2010/11 Household Consumption Expenditure (HCE) survey and the 2007 census data sets. Corresponding Author: Yegnanew A Shiferaw (yegnanews@uj.ac.za) Jacqueline S Galpin (Jacqueline.Galpin@wits.ac.za). D ow nl oa de d fr om ji rs s. irs ta t.i r at 0 :5 4 + 03 30 o n T hu rs da y D ec em be r 6t h 20 18 [ D O I: 10 .1 88 69 /a ca dp ub .ji rs s. 15 .2 .1 ] 2 Shiferaw and Galpin","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"10 1","pages":"1-43"},"PeriodicalIF":0.4,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67675046","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 : 2016-08-25DOI: 10.18869/ACADPUB.JIRSS.15.2.45
Vahid Rezaei Tabar, M. Mahdavi, S. Heidari, S. Naghizadeh
{"title":"Learning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis","authors":"Vahid Rezaei Tabar, M. Mahdavi, S. Heidari, S. Naghizadeh","doi":"10.18869/ACADPUB.JIRSS.15.2.45","DOIUrl":"https://doi.org/10.18869/ACADPUB.JIRSS.15.2.45","url":null,"abstract":"","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"15 1","pages":"45-61"},"PeriodicalIF":0.4,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67675272","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 : 2015-12-25DOI: 10.7508/JIRSS.2015.02.002
A. Ghodsi
{"title":"Conditional Maximum Likelihood Estimation of the First-Order Spatial Integer-Valued Autoregressive (SINAR(1,1)) Model","authors":"A. Ghodsi","doi":"10.7508/JIRSS.2015.02.002","DOIUrl":"https://doi.org/10.7508/JIRSS.2015.02.002","url":null,"abstract":"","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"9 1","pages":"15-36"},"PeriodicalIF":0.4,"publicationDate":"2015-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71112309","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}
In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. This new class contains some new sub-models such as the bivariate generalized exponential distribution, the bivariate generalized exponential-poisson, -logarithmic, -binomial and -negative binomial distributions. We derive different properties of the new class of distributions. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We illustrate the usefulness of the new distributions by means of an application to a real data set.
{"title":"On Bivariate Generalized Exponential-Power Series Class of Distributions","authors":"A. Jafari, R. Roozegar","doi":"10.29252/JIRSS.17.1.63","DOIUrl":"https://doi.org/10.29252/JIRSS.17.1.63","url":null,"abstract":"In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. \u0000This new class contains some new sub-models such as the bivariate generalized exponential distribution, the bivariate generalized exponential-poisson, -logarithmic, -binomial and -negative binomial distributions. We derive different properties of the new class of distributions. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We illustrate the usefulness of the new distributions by means of an application to a real data set.","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"8 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2015-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69861192","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}
A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Maximum likelihood estimators of the parameters of the marginal distribution functions associated with each of the competing risks and also of the system lifetime distribution function are presented. Estimators are derived under perfect and partial repair strategies. Consistency and asymptotic properties of the estimators are obtained. The estimation methods are applied to a data set of failures for cars under warranty. Simulation studies are used to ascertain the small sample properties and the efficiency gains of the resulting estimators.
{"title":"Parametric Estimation in a Recurrent Competing Risks Model.","authors":"Laura L Taylor, Edsel A Peña","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Maximum likelihood estimators of the parameters of the marginal distribution functions associated with each of the competing risks and also of the system lifetime distribution function are presented. Estimators are derived under perfect and partial repair strategies. Consistency and asymptotic properties of the estimators are obtained. The estimation methods are applied to a data set of failures for cars under warranty. Simulation studies are used to ascertain the small sample properties and the efficiency gains of the resulting estimators.</p>","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"12 1","pages":"153-181"},"PeriodicalIF":0.4,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4206915/pdf/nihms585707.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32773594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoreti- cal basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis and as an extension to the early paper of (19).
{"title":"Parametric and Nonparametric Regression with Missing X’s—A Review","authors":"H. Toutenburg, C. Heumann, T. Nittner, S. Scheid","doi":"10.5282/UBM/EPUB.1664","DOIUrl":"https://doi.org/10.5282/UBM/EPUB.1664","url":null,"abstract":"This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoreti- cal basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis and as an extension to the early paper of (19).","PeriodicalId":42965,"journal":{"name":"JIRSS-Journal of the Iranian Statistical Society","volume":"1 1","pages":"79-109"},"PeriodicalIF":0.4,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71098613","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：