Pub Date : 2018-10-02DOI: 10.1080/08898480.2018.1507580
E. Fabrizi, Tomasz Żądło
This issue is devoted to survey sampling methods. It carries on a tradition of Mathematical Population Studies, after the issues guest-edited by Malay Ghosh and Tomasz Ża̧dło (2014) and Vera Toepoel and Schonlau (2017). Wright (2001) presented some major moments of the history of survey sampling. He acknowledged the pioneering work of Pierre Simon de Laplace (1878-1912; Gillispie, 1997), who estimated the population size of France in 1802 based on a sample of communes, which were administrative districts. He multiplied the population size of the sampled communes by the ratio of the recorded total number of births for the whole country to the one recorded in the sample. He used the same method to estimate the population size of France for 1782. John Graunt (1665) had also used a similar calculus to estimate the population size of England in 1662. In design-based inference, introduced by Neyman (1934), the values taken by the variable of interest are considered as fixed and the sampling design is the only source of randomness affecting the estimates. In modelbased inference, the values taken by the variable of interest are considered as the realizations of random variables. The set of conditions defining the class of this distribution is called “super-population” model (Cassel et al., 1976: 80) and inference is made conditionally on the sample, which is either drawn at random or chosen purposively from the population. Accuracy is measured only over possible realizations of the variables. In model-assisted inference, the model is used to increase accuracy, but good design-based properties, such as design consistency, are of primary interest. Various methods include calibration estimators and pseudo-empirical best linear unbiased predictors. The accuracy of the former is evaluated through randomization techniques; the accuracy of the latter through a model. In the Bayesian framework, the estimator is a conditional expectation in the posterior distribution of the population or subpopulation parameters and the posterior variance is used as a measure of the variability of the Bayesian estimator. This Bayesian technique applies to continuous, binary, and count data.
{"title":"Survey sampling and small-area estimation","authors":"E. Fabrizi, Tomasz Żądło","doi":"10.1080/08898480.2018.1507580","DOIUrl":"https://doi.org/10.1080/08898480.2018.1507580","url":null,"abstract":"This issue is devoted to survey sampling methods. It carries on a tradition of Mathematical Population Studies, after the issues guest-edited by Malay Ghosh and Tomasz Ża̧dło (2014) and Vera Toepoel and Schonlau (2017). Wright (2001) presented some major moments of the history of survey sampling. He acknowledged the pioneering work of Pierre Simon de Laplace (1878-1912; Gillispie, 1997), who estimated the population size of France in 1802 based on a sample of communes, which were administrative districts. He multiplied the population size of the sampled communes by the ratio of the recorded total number of births for the whole country to the one recorded in the sample. He used the same method to estimate the population size of France for 1782. John Graunt (1665) had also used a similar calculus to estimate the population size of England in 1662. In design-based inference, introduced by Neyman (1934), the values taken by the variable of interest are considered as fixed and the sampling design is the only source of randomness affecting the estimates. In modelbased inference, the values taken by the variable of interest are considered as the realizations of random variables. The set of conditions defining the class of this distribution is called “super-population” model (Cassel et al., 1976: 80) and inference is made conditionally on the sample, which is either drawn at random or chosen purposively from the population. Accuracy is measured only over possible realizations of the variables. In model-assisted inference, the model is used to increase accuracy, but good design-based properties, such as design consistency, are of primary interest. Various methods include calibration estimators and pseudo-empirical best linear unbiased predictors. The accuracy of the former is evaluated through randomization techniques; the accuracy of the latter through a model. In the Bayesian framework, the estimator is a conditional expectation in the posterior distribution of the population or subpopulation parameters and the posterior variance is used as a measure of the variability of the Bayesian estimator. This Bayesian technique applies to continuous, binary, and count data.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1507580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46494779","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 : 2018-10-02DOI: 10.1080/08898480.2018.1508189
Tomasz Ba̧k
ABSTRACT In drawn-by-drawn sampling, elements are drawn one after another and drawing can be stopped at any time. It leads to ordered samples. This method is convenient to obtain spatially balanced samples. However, sampling may not need to be unordered. This is the case of Wywiał sampling designs, which are based on a neighborhood matrix. Their adaptation to drawn-by-drawn sampling has the merit to be of simple use. It requires defining the sampling plan, the sampling scheme, and the first-order probabilities of inclusion. Application to a sampling from a grid of squares.
{"title":"Drawn-by-drawn sampling based on neighborhood matrix","authors":"Tomasz Ba̧k","doi":"10.1080/08898480.2018.1508189","DOIUrl":"https://doi.org/10.1080/08898480.2018.1508189","url":null,"abstract":"ABSTRACT In drawn-by-drawn sampling, elements are drawn one after another and drawing can be stopped at any time. It leads to ordered samples. This method is convenient to obtain spatially balanced samples. However, sampling may not need to be unordered. This is the case of Wywiał sampling designs, which are based on a neighborhood matrix. Their adaptation to drawn-by-drawn sampling has the merit to be of simple use. It requires defining the sampling plan, the sampling scheme, and the first-order probabilities of inclusion. Application to a sampling from a grid of squares.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1508189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42484303","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 : 2018-10-02DOI: 10.1080/08898480.2018.1493867
Akbar Abravesh, M. Ganji, Behdad Mostafaiy
ABSTRACT For and two independent random variables, upper values from the family of distributions with power hazard function are used to obtain the maximum likelihood and the Bayes estimators of . The Bayes estimator relies on the squared-error loss function given informative and non-informative prior distributions. It is obtained by either Lindley’s approximation, Tierney and Kadane’s method, or Monte Carlo simulation. The Monte Carlo simulation and Tierney and Kadane’s method have smaller mean squared errors than both Lindley’s approximation and the maximum likelihood estimator. The application for lung cancer data shows that the mortality risk by lung cancer is 40% lower for men than for women. The application for lifetimes of steels shows that steel specimen are 40% more likely to break up under 35.0 stress amplitude than under 35.5.
{"title":"Estimation of reliability P(X > Y) for distributions with power hazard function based on upper record values","authors":"Akbar Abravesh, M. Ganji, Behdad Mostafaiy","doi":"10.1080/08898480.2018.1493867","DOIUrl":"https://doi.org/10.1080/08898480.2018.1493867","url":null,"abstract":"ABSTRACT For and two independent random variables, upper values from the family of distributions with power hazard function are used to obtain the maximum likelihood and the Bayes estimators of . The Bayes estimator relies on the squared-error loss function given informative and non-informative prior distributions. It is obtained by either Lindley’s approximation, Tierney and Kadane’s method, or Monte Carlo simulation. The Monte Carlo simulation and Tierney and Kadane’s method have smaller mean squared errors than both Lindley’s approximation and the maximum likelihood estimator. The application for lung cancer data shows that the mortality risk by lung cancer is 40% lower for men than for women. The application for lifetimes of steels shows that steel specimen are 40% more likely to break up under 35.0 stress amplitude than under 35.5.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1493867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43066410","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 : 2018-09-14DOI: 10.1080/08898480.2018.1493869
Xiaojie Mu, Qimin Zhang, Hanna Wu, Xining Li
ABSTRACT An epidemic model with stochastic contact transmission coefficient takes into account white noise and the influence of information. Sufficient conditions for the extinction and persistence of the disease are expressed. The existence of a stationary distribution and the ergodic property are proved. The peak of infected population can be decreased by information. The analytical results are showed by simulations and the influence of white noise and information on the dynamics of epidemics are evaluated.
{"title":"Ergodicity and extinction in a stochastic susceptible-infected-recovered-susceptible epidemic model with influence of information","authors":"Xiaojie Mu, Qimin Zhang, Hanna Wu, Xining Li","doi":"10.1080/08898480.2018.1493869","DOIUrl":"https://doi.org/10.1080/08898480.2018.1493869","url":null,"abstract":"ABSTRACT An epidemic model with stochastic contact transmission coefficient takes into account white noise and the influence of information. Sufficient conditions for the extinction and persistence of the disease are expressed. The existence of a stationary distribution and the ergodic property are proved. The peak of infected population can be decreased by information. The analytical results are showed by simulations and the influence of white noise and information on the dynamics of epidemics are evaluated.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1493869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48302568","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 : 2018-09-12DOI: 10.1080/08898480.2018.1477385
Barbara Kowalczyk, D. Juszczak
ABSTRACT Partial replacement of units in repeated surveys increases the efficiency of the estimation of the population mean. The composite estimator with constant coefficients, based on the recursive ratio, is useful in surveys with many variables. The mean square error of this estimator is obtained for an arbitrary rotation scheme. Comparisons indicate that it is more efficient than the sample mean for various rotation schemes. Simulations show that it performs better than other composite estimators in surveys with many variables changing differently over time.
{"title":"Composite estimator based on the recursive ratio for an arbitrary rotation scheme","authors":"Barbara Kowalczyk, D. Juszczak","doi":"10.1080/08898480.2018.1477385","DOIUrl":"https://doi.org/10.1080/08898480.2018.1477385","url":null,"abstract":"ABSTRACT Partial replacement of units in repeated surveys increases the efficiency of the estimation of the population mean. The composite estimator with constant coefficients, based on the recursive ratio, is useful in surveys with many variables. The mean square error of this estimator is obtained for an arbitrary rotation scheme. Comparisons indicate that it is more efficient than the sample mean for various rotation schemes. Simulations show that it performs better than other composite estimators in surveys with many variables changing differently over time.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1477385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41532075","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 : 2018-08-16DOI: 10.1080/08898480.2020.1767411
E. Chernousova, Yaqin Feng, O. Hryniv, S. Molchanov, Joseph Whitmeyer
ABSTRACT In a lattice population model where individuals evolve as subcritical branching random walks subject to external immigration, the cumulants are estimated and the existence of the steady state is proved. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model. An explicit formula of the limit distribution is derived in the solvable case of no birth. Monte Carlo simulation shows the limit distribution in the solvable case.
{"title":"Steady states of lattice population models with immigration","authors":"E. Chernousova, Yaqin Feng, O. Hryniv, S. Molchanov, Joseph Whitmeyer","doi":"10.1080/08898480.2020.1767411","DOIUrl":"https://doi.org/10.1080/08898480.2020.1767411","url":null,"abstract":"ABSTRACT In a lattice population model where individuals evolve as subcritical branching random walks subject to external immigration, the cumulants are estimated and the existence of the steady state is proved. The resulting dynamics are Lyapunov stable in that their qualitative behavior does not change under suitable perturbations of the main parameters of the model. An explicit formula of the limit distribution is derived in the solvable case of no birth. Monte Carlo simulation shows the limit distribution in the solvable case.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2020.1767411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42274757","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 : 2018-07-27DOI: 10.1080/08898480.2018.1437318
Mauno Keto, Jussi Hakanen, Erkki Pahkinen
ABSTRACT The inadequate control of sample sizes in surveys using stratified sampling and area estimation may occur when the overall sample size is small or auxiliary information is insufficiently used. Very small sample sizes are possible for some areas. The proposed allocation based on multi-objective optimization uses a small-area model and estimation method and semi-collected empirical data annually collected empirical data. The assessment of its performance at the area and at the population levels is based on design-based sample simulations. Five previously developed allocations serve as references. The model-based estimator is more accurate than the design-based Horvitz–Thompson estimator and the model-assisted regression estimator. Two trade-off issues are between accuracy and bias and between the area- and the population-level qualities of estimates. If the survey uses model-based estimation, the sampling design should incorporate the underlying model and the estimation method.
{"title":"Register data in sample allocations for small-area estimation","authors":"Mauno Keto, Jussi Hakanen, Erkki Pahkinen","doi":"10.1080/08898480.2018.1437318","DOIUrl":"https://doi.org/10.1080/08898480.2018.1437318","url":null,"abstract":"ABSTRACT The inadequate control of sample sizes in surveys using stratified sampling and area estimation may occur when the overall sample size is small or auxiliary information is insufficiently used. Very small sample sizes are possible for some areas. The proposed allocation based on multi-objective optimization uses a small-area model and estimation method and semi-collected empirical data annually collected empirical data. The assessment of its performance at the area and at the population levels is based on design-based sample simulations. Five previously developed allocations serve as references. The model-based estimator is more accurate than the design-based Horvitz–Thompson estimator and the model-assisted regression estimator. Two trade-off issues are between accuracy and bias and between the area- and the population-level qualities of estimates. If the survey uses model-based estimation, the sampling design should incorporate the underlying model and the estimation method.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1437318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48956783","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 : 2018-07-27DOI: 10.1080/08898480.2017.1418114
Angela Chieppa, G. Gallo, Valeria Tomeo, Francesco Borrelli, S. Di Domenico
ABSTRACT From 2018 onward, the population census in Italy will leave the traditional “door-to-door” enumeration for a “register-based” system combining administrative data and surveys. An integrated system of registers makes it possible to identify patterns and groups among huge amounts of administrative data. The Italian National Institute of Statistics (Istat) carried out a trial to compute the usually resident population by using administrative data and identify patterns, leading to classify individuals and constitute groups, in order to prepare the register-based census.
{"title":"Knowledge discovery for inferring the usually resident population from administrative registers","authors":"Angela Chieppa, G. Gallo, Valeria Tomeo, Francesco Borrelli, S. Di Domenico","doi":"10.1080/08898480.2017.1418114","DOIUrl":"https://doi.org/10.1080/08898480.2017.1418114","url":null,"abstract":"ABSTRACT From 2018 onward, the population census in Italy will leave the traditional “door-to-door” enumeration for a “register-based” system combining administrative data and surveys. An integrated system of registers makes it possible to identify patterns and groups among huge amounts of administrative data. The Italian National Institute of Statistics (Istat) carried out a trial to compute the usually resident population by using administrative data and identify patterns, leading to classify individuals and constitute groups, in order to prepare the register-based census.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2017.1418114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42566271","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 : 2018-07-03DOI: 10.1080/08898480.2018.1477384
P. Polyakov
ABSTRACT The ice bucket challenge is a social game aimed at encouraging donations to the amyotrophic lateral sclerosis association. The rules imply that each participant challenges each recruited follower to dump a bucket of ice water on his or her head. The network of who has nominated whom has a tree structure. The short duration of the ice bucket challenge is explained by using the reproduction number , under the assumption that the capacity to recruit followers varies with the participant. The epidemic lasts until the interruption of the transmission tree occurring well before the depletion of susceptible followers. Such a tree is reconstructed from publicly available contact data and the interest in this game.
{"title":"Termination of the ice bucket challenge","authors":"P. Polyakov","doi":"10.1080/08898480.2018.1477384","DOIUrl":"https://doi.org/10.1080/08898480.2018.1477384","url":null,"abstract":"ABSTRACT The ice bucket challenge is a social game aimed at encouraging donations to the amyotrophic lateral sclerosis association. The rules imply that each participant challenges each recruited follower to dump a bucket of ice water on his or her head. The network of who has nominated whom has a tree structure. The short duration of the ice bucket challenge is explained by using the reproduction number , under the assumption that the capacity to recruit followers varies with the participant. The epidemic lasts until the interruption of the transmission tree occurring well before the depletion of susceptible followers. Such a tree is reconstructed from publicly available contact data and the interest in this game.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1477384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47744216","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 : 2018-07-03DOI: 10.1080/08898480.2018.1428469
M. Ghosh, Jiyoun Myung, P. Sankaran
ABSTRACT Nonparametric Bayes and empirical Bayes estimators of the population median are provided under Dirichlet process priors. The finite-population sampling is used to estimate the finite-population median under Dirichlet process priors. The asymptotic properties of the estimators are obtained from a frequentist perspective.
{"title":"Nonparametric Bayes and empirical Bayes estimation of the population median, with application in finite-population sampling","authors":"M. Ghosh, Jiyoun Myung, P. Sankaran","doi":"10.1080/08898480.2018.1428469","DOIUrl":"https://doi.org/10.1080/08898480.2018.1428469","url":null,"abstract":"ABSTRACT Nonparametric Bayes and empirical Bayes estimators of the population median are provided under Dirichlet process priors. The finite-population sampling is used to estimate the finite-population median under Dirichlet process priors. The asymptotic properties of the estimators are obtained from a frequentist perspective.","PeriodicalId":49859,"journal":{"name":"Mathematical Population Studies","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08898480.2018.1428469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42312238","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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