Pub Date : 2019-10-30DOI: 10.1080/08898480.2019.1681187
M. E. El-Adll
ABSTRACT Inferences about estimation and prediction of the two-parameter exponential distribution are based on generalized order statistics. Point and interval estimates are used for scale and location parameters. Unbiased point predictors and reconstructors are based upon pivotal quantities. The mean square error and Pitman’s measure help assess the closeness of estimators and predictors. Point estimators of scale and location parameters and point predictors of future observations are computed in the application of durations until remission of 20 leukemia patients and in the application until failure of air-conditioning systems.
{"title":"Inference for the two-parameter exponential distribution with generalized order statistics","authors":"M. E. El-Adll","doi":"10.1080/08898480.2019.1681187","DOIUrl":"https://doi.org/10.1080/08898480.2019.1681187","url":null,"abstract":"ABSTRACT Inferences about estimation and prediction of the two-parameter exponential distribution are based on generalized order statistics. Point and interval estimates are used for scale and location parameters. Unbiased point predictors and reconstructors are based upon pivotal quantities. The mean square error and Pitman’s measure help assess the closeness of estimators and predictors. Point estimators of scale and location parameters and point predictors of future observations are computed in the application of durations until remission of 20 leukemia patients and in the application until failure of air-conditioning systems.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"28 1","pages":"1 - 23"},"PeriodicalIF":1.8,"publicationDate":"2019-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1681187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-02DOI: 10.1080/08898480.2019.1653058
S. Matthews
The two thematic issues 26(4) and 27(1) of Mathematical Population Studies deal with “methods and applications in spatial demography.” The five articles they contain, and which are listed below, show how population studies can be informed through an integration of appropriate spatial theory, data, and methods. In the editorial to appear in the next issue, 27(1), I will provide a summary of each article and discuss its contribution to this very lively field. I would like to thank the authors, the journal, and Taylor and Francis for these issues, which I had the pleasure of organizing. We wish you a pleasant reading.
{"title":"Methods and applications in spatial demography","authors":"S. Matthews","doi":"10.1080/08898480.2019.1653058","DOIUrl":"https://doi.org/10.1080/08898480.2019.1653058","url":null,"abstract":"The two thematic issues 26(4) and 27(1) of Mathematical Population Studies deal with “methods and applications in spatial demography.” The five articles they contain, and which are listed below, show how population studies can be informed through an integration of appropriate spatial theory, data, and methods. In the editorial to appear in the next issue, 27(1), I will provide a summary of each article and discuss its contribution to this very lively field. I would like to thank the authors, the journal, and Taylor and Francis for these issues, which I had the pleasure of organizing. We wish you a pleasant reading.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"26 1","pages":"183 - 184"},"PeriodicalIF":1.8,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1653058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47249999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-02DOI: 10.1080/08898480.2019.1669363
S. Matthews
{"title":"In memoriam: Jennifer Buher Kane (1979–2019)","authors":"S. Matthews","doi":"10.1080/08898480.2019.1669363","DOIUrl":"https://doi.org/10.1080/08898480.2019.1669363","url":null,"abstract":"","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"26 1","pages":"185 - 185"},"PeriodicalIF":1.8,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1669363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44556794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-09DOI: 10.1080/08898480.2019.1656491
Chouaib Beldjoudi, T. Kernane, Hamid El Maroufy
ABSTRACT A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.
{"title":"Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation","authors":"Chouaib Beldjoudi, T. Kernane, Hamid El Maroufy","doi":"10.1080/08898480.2019.1656491","DOIUrl":"https://doi.org/10.1080/08898480.2019.1656491","url":null,"abstract":"ABSTRACT A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"232 - 258"},"PeriodicalIF":1.8,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1656491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43385656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-25DOI: 10.1080/08898480.2019.1636574
Zack W. Almquist, Nathaniel E. Helwig, Yun You
ABSTRACT In 2007, the Department of Housing and Urban Development initiated a point-in-time count of the homeless across the United States. The counts are administered by the Continuum of Care Program, which provides spatial and temporal data for the homeless population over the last decade. Unfortunately, this administrative spatial unit does not align with the more common areal units defined by the United States Census Bureau, which limits usability of these data. To unify these two areal units, spatial disaggregation, matching, and imputation allow for aligning Continuum of Care data with county data. The resulting county-level homeless counts for the years 2005 to 2017 are provided as an R package. The county-level data display more spatial precision and more temporal variation than the Continuum of Care-level data. Nonparametric regression analyses reveal that the spatiotemporal variation in the data can be well approximated by additive spatial and temporal effects at both the county and Continuum of Care level.
2007年，美国住房和城市发展部发起了一项针对全美无家可归者的时点统计。这些统计是由连续关怀计划管理的，该计划提供了过去十年无家可归人口的空间和时间数据。不幸的是，这个行政空间单位与美国人口普查局定义的更常见的面积单位不一致，这限制了这些数据的可用性。为了统一这两个区域单位，空间分解、匹配和imputation允许将Continuum of Care数据与县数据对齐。由此产生的2005年至2017年县级无家可归者统计数据作为R包提供。县级数据的空间精度和时间变异性均高于关爱级连续体数据。非参数回归分析表明，在县域和连续关怀水平上，数据的时空变化可以很好地近似于加性时空效应。
{"title":"Connecting Continuum of Care point-in-time homeless counts to United States Census areal units","authors":"Zack W. Almquist, Nathaniel E. Helwig, Yun You","doi":"10.1080/08898480.2019.1636574","DOIUrl":"https://doi.org/10.1080/08898480.2019.1636574","url":null,"abstract":"ABSTRACT In 2007, the Department of Housing and Urban Development initiated a point-in-time count of the homeless across the United States. The counts are administered by the Continuum of Care Program, which provides spatial and temporal data for the homeless population over the last decade. Unfortunately, this administrative spatial unit does not align with the more common areal units defined by the United States Census Bureau, which limits usability of these data. To unify these two areal units, spatial disaggregation, matching, and imputation allow for aligning Continuum of Care data with county data. The resulting county-level homeless counts for the years 2005 to 2017 are provided as an R package. The county-level data display more spatial precision and more temporal variation than the Continuum of Care-level data. Nonparametric regression analyses reveal that the spatiotemporal variation in the data can be well approximated by additive spatial and temporal effects at both the county and Continuum of Care level.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"46 - 58"},"PeriodicalIF":1.8,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1636574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42686396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-03DOI: 10.1080/08898480.2019.1626189
E. Chernousova, O. Hryniv, S. Molchanov
ABSTRACT In a population model in continuous space, individuals evolve independently as branching random walks subject to immigration. If the underlying branching mechanism is subcritical, the model has a unique steady state for each value of the immigration intensity. Convergence to the equilibrium is exponentially fast. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model.
{"title":"Population model with immigration in continuous space","authors":"E. Chernousova, O. Hryniv, S. Molchanov","doi":"10.1080/08898480.2019.1626189","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626189","url":null,"abstract":"ABSTRACT In a population model in continuous space, individuals evolve independently as branching random walks subject to immigration. If the underlying branching mechanism is subcritical, the model has a unique steady state for each value of the immigration intensity. Convergence to the equilibrium is exponentially fast. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"199 - 215"},"PeriodicalIF":1.8,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42375749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-19DOI: 10.1080/08898480.2019.1626635
Saurav Guha, Hukum Chandra
ABSTRACT Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved chain-ratio estimator for the population total based on double sampling is proposed when auxiliary information is available for the first variable and not available for the second variable. The bias and the mean square error of this estimator are obtained for a large sample. Empirical evaluations using both model-based and design-based simulations show that the proposed estimator performs better than the ratio, the regression, and the difference estimators.
{"title":"Improved chain-ratio type estimator for population total in double sampling","authors":"Saurav Guha, Hukum Chandra","doi":"10.1080/08898480.2019.1626635","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626635","url":null,"abstract":"ABSTRACT Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved chain-ratio estimator for the population total based on double sampling is proposed when auxiliary information is available for the first variable and not available for the second variable. The bias and the mean square error of this estimator are obtained for a large sample. Empirical evaluations using both model-based and design-based simulations show that the proposed estimator performs better than the ratio, the regression, and the difference estimators.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"216 - 231"},"PeriodicalIF":1.8,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48595379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1080/08898480.2019.1626633
Xiaoni Li, Xining Li, Qimin Zhang
ABSTRACT A stochastic susceptible-infected-recovered-susceptible model with vaccination includes stochastic variation in its parameters. Sufficient conditions for the extinction and the existence of the stationary distribution of the population are proved.
{"title":"Time to extinction and stationary distribution of a stochastic susceptible-infected-recovered-susceptible model with vaccination under Markov switching","authors":"Xiaoni Li, Xining Li, Qimin Zhang","doi":"10.1080/08898480.2019.1626633","DOIUrl":"https://doi.org/10.1080/08898480.2019.1626633","url":null,"abstract":"ABSTRACT A stochastic susceptible-infected-recovered-susceptible model with vaccination includes stochastic variation in its parameters. Sufficient conditions for the extinction and the existence of the stationary distribution of the population are proved.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"259 - 274"},"PeriodicalIF":1.8,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1626633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45208367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-21DOI: 10.1080/08898480.2019.1592638
Gillian Dunn, Glen D. Johnson, D. Balk, Grace Sembajwe
ABSTRACT Diarrhea is a major contributor to child morbidity and mortality in West Africa. Non-spatial regression and geographically weighted Poisson regression applied to data from 10 Demographic and Health Surveys conducted in West Africa from 2008 to 2013 show that water source, toilet type, mother’s education, latitude, temperature, rainfall, altitude, and population density influence the risk of diarrhea. The risk associated with these factors is dependent on location and may be higher or lower than the rest of the study area. Areas with increased relative risk for diarrhea include several urban centers, low-elevation areas (coastal and along rivers), remote areas such as western Mali, and conflict zones (northeast Nigeria).
{"title":"Spatially varying relationships between risk factors and child diarrhea in West Africa, 2008-2013","authors":"Gillian Dunn, Glen D. Johnson, D. Balk, Grace Sembajwe","doi":"10.1080/08898480.2019.1592638","DOIUrl":"https://doi.org/10.1080/08898480.2019.1592638","url":null,"abstract":"ABSTRACT Diarrhea is a major contributor to child morbidity and mortality in West Africa. Non-spatial regression and geographically weighted Poisson regression applied to data from 10 Demographic and Health Surveys conducted in West Africa from 2008 to 2013 show that water source, toilet type, mother’s education, latitude, temperature, rainfall, altitude, and population density influence the risk of diarrhea. The risk associated with these factors is dependent on location and may be higher or lower than the rest of the study area. Areas with increased relative risk for diarrhea include several urban centers, low-elevation areas (coastal and along rivers), remote areas such as western Mali, and conflict zones (northeast Nigeria).","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"27 1","pages":"8 - 33"},"PeriodicalIF":1.8,"publicationDate":"2019-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2019.1592638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47205859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-03DOI: 10.1080/08898480.2017.1418113
Biagio Aragona, R. De Rosa
ABSTRACT A review of studies based on big data shows that big data advantageously complete surveys and censuses, nurture policy making, and highlight effects of a given policy in real time.
基于大数据的研究综述表明，大数据有利于实时完成调查和普查，培育政策制定，并突出特定政策的效果。
{"title":"Big data in policy making","authors":"Biagio Aragona, R. De Rosa","doi":"10.1080/08898480.2017.1418113","DOIUrl":"https://doi.org/10.1080/08898480.2017.1418113","url":null,"abstract":"ABSTRACT A review of studies based on big data shows that big data advantageously complete surveys and censuses, nurture policy making, and highlight effects of a given policy in real time.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":"26 1","pages":"107 - 113"},"PeriodicalIF":1.8,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2017.1418113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45676558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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