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Calcutta Statistical Association Bulletin
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Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0008-0683 - ISSN (Online) 2456-6462
Published by Sage Publications

[1176 journals]

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  Authors: William R. Bell
  Pages: 33 - 38
  Abstract: Calcutta Statistical Association Bulletin, Volume 76, Issue 1, Page 33-38, May 2024.
  
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-06-21T08:13:31Z
  DOI: 10.1177/00080683241253104
  Issue No: Vol. 76, No. 1 (2024)
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  Authors: Stephen Haslett
  Pages: 39 - 51
  Abstract: Calcutta Statistical Association Bulletin, Volume 76, Issue 1, Page 39-51, May 2024.
  
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-06-21T08:13:32Z
  DOI: 10.1177/00080683241239211
  Issue No: Vol. 76, No. 1 (2024)
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  Authors: Partha Lahiri
  Pages: 52 - 56
  Abstract: Calcutta Statistical Association Bulletin, Volume 76, Issue 1, Page 52-56, May 2024.
  
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-06-21T08:13:34Z
  DOI: 10.1177/00080683241255729
  Issue No: Vol. 76, No. 1 (2024)
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  Authors: David Newhouse
  Pages: 57 - 61
  Abstract: Calcutta Statistical Association Bulletin, Volume 76, Issue 1, Page 57-61, May 2024.
  
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-06-21T08:13:35Z
  DOI: 10.1177/00080683241255897
  Issue No: Vol. 76, No. 1 (2024)
Distribution of the Joint Survival Function of an Archimedean Copula
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  Authors: Magloire Loudegui Djimdou, Yogendra P. Chaubey, Arusharka Sen
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  Suppose a random vector [math] with values in the unit cube has a joint survival function: C*u1,…,ud=ℙU1>u1,…,Ud>ud,given by an Archimedean copula Cu1,…,ud=φ−1φu1+…+φud,with generator [math], a smooth decreasing convex function such that [math]. In this article, we provide a formula for the distribution of Z=C*U1,…,Ud=ℙU1'>U1,…,Ud'>Ud∣U1,…,Ud,where [math] is an independent copy of [math] and a method to simulate values from the distribution of Z in the bivariate case, that is, when d = 2. The case d> 2 does not seem to be tractable. As an application, we show how our result can be used to compute the limiting covariance of the empirical Kendall process corresponding to [math].AMS Subject Classification: 62H05
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-06-12T07:21:13Z
  DOI: 10.1177/00080683241246438
Mean Square Error Estimation of Small Area Predictors by Use of Parametric
and Nonparametric Bootstrap
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  Authors: Danny Pfeffermann, Hagit Glickman, Arie Preminger
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  In this article, we propose and compare some old and new parametric and nonparametric bootstrap methods for MSE estimation in small area estimation, restricting to the case of the widely used Fay-Herriot model. The parametric method consists of generating parametrically a large number of area bootstrap samples from the model fitted to the original data, re-estimating the model parameters for each bootstrap sample and then estimating the separate components of the MSE. The use of double-bootstrap is also considered. The nonparametric method generates the samples by bootstrapping standardized residuals, estimated from the original sample data. The bootstrap procedures are compared to other methods proposed in the literature in a simulation study, which also examines the robustness of the various methods to non-normality of the model error terms. A design-based MSE estimator for the Fay-Herriot model-dependent predictor is also described and its performance is investigated in a separate simulation study.AMS subject classification: 62F10, 62F40
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-03-01T07:17:20Z
  DOI: 10.1177/00080683231203823
Small area estimates for Voting Rights Act Section 203(b) coverage
determinations
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  Authors: Eric Slud, Carolina Franco, Adam Hall
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  The Director of the US Census Bureau every five years, under the Voting Rights Act (VRA) Section 203(b), determines which states and political subdivisions must provide ballot assistance in languages other than English. These rule-based determinations use small-area estimates of the proportions of voting-age citizen members of racial/ethnic Language Minority Groups (LMGs) who are limited Englishproficient (LEP) and of LEP LMG persons who are also illiterate. This large-scale Small Area Estimation (SAE) effort by the US Census Bureau is based on American Community Survey five-year data. This paper focuses on the unique attributes of this SAE problem, including the predominance in each LMG of tiny domains along with relatively few large domains that account for the bulk of LMG citizens. The data and small area models are treated separately for distinct LMGs, and the spirit of the law requires that the domain estimates should be produced in a similar way across LMGs for each type of geography (Jurisdictions, which are mostly counties, plus two types of American Indian and Alaskan Native areas). The paper describes and assesses the SAE models developed under unique constraints, including novel methodological aspects and the use of hybrid frequentist and Bayesian computational techniques; situates these modelling choices in the broader literature on SAE; and discusses the strengths and weaknesses of the resulting models.
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-02-14T06:34:17Z
  DOI: 10.1177/00080683231215985
An Area-Level Hierarchical Bayes Bivariate Threefold Linear Model for US
State and County Indicators of Adult Skills in Literacy and Numeracy
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  Authors: Tom Krenzke, Andreea L. Erciulescu, Leyla Mohadjer, Robert Fay, Jianzhu Li
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  Significant enhancements have been made in small area estimation (SAE) methodology because there has been an increased demand for reliable estimates through SAE over the past decades. This article describes the advanced statistical methodology used to produce state and county model-based estimates of average scores and various proficiency levels of adults for all states and counties, using data from the first cycle of the US Program for the International Assessment of Adult Competencies and the American Community Survey. Challenges and issues are discussed especially in light of the small number of sample counties with survey data. Each major stage of the estimation process is discussed, including the approach for generating design-based survey estimates and modelled variances, identifying a set of predictor variables (available and measured consistently for all counties), the hierarchical Bayes linear threefold models, including bivariate models for proportions, and the diagnostics and evaluation.
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-02-07T11:20:47Z
  DOI: 10.1177/00080683231199020
Estimation of Poverty Measures for Small Areas Under a Two-fold Nested
Error Linear Regression Model: Comparison of Two Methods
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  Authors: Maryam Sohrabi, J. N. K. Rao
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  Demand for reliable statistics at a local area (small area) level has greatly increased in recent years. Traditional area-specific estimators based on probability samples are not adequate because of small sample size or even zero sample size in a local area. As a result, methods based on models linking the areas are widely used. World Bank focused on estimating poverty measures, in particular poverty incidence and poverty gap called FGT measures, using a simulated census method, called ELL, based on a one-fold nested error model for a suitable transformation of the welfare variable. Modified ELL methods leading to significant gain in efficiency over ELL also have been proposed under the one-fold model. An advantage of ELL and modified ELL methods is that distributional assumptions on the random effects in the model are not needed. In this article, we extend ELL and modified ELL to two-fold nested error models to estimate poverty indicators for areas (say a state) and subareas (say counties within a state). Our simulation results indicate that the modified ELL estimators lead to large efficiency gains over ELL at the area level and subarea level. Further, modified ELL method retaining both area and subarea estimated effects in the model (called MELL2) performs significantly better in terms of mean squared error (MSE) for sampled subareas than the modified ELL retaining only estimated area effect in the model (called MELLI).AMS Subject Classification: 62D05, 62G09
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2024-01-10T01:33:26Z
  DOI: 10.1177/00080683231210426
A quasi-Bayesian Approach to Small Area Estimation Using Spatial Models
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  Authors: Jiacheng Li, Gauri S. Datta
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  The empirical best linear unbiased prediction (EBLUP) method has been the dominant model-based approach in small area estimation. As an alternative to this frequentist method, the observed best prediction (OBP) method, also frequentist, was proposed by Jiang et al.[] where the parameters of the model are estimated by minimizing an objective function which is implied by the total mean squared prediction error. In a recent article, Datta et al.[] followed a general Bayesian approach, proposed recently by Bissiri et al.[], to develop a quasi-Bayesian method by appropriately calibrating the objective function for the OBP method for the Fay-Herriot model. In a different article, Chung and Datta[] demonstrated that in the absence of covariates with good predictive power the small area estimates from the standard Fay-Herriot model can be improved by using spatially dependent random effects. In this article, we develop a quasi-Bayesian small area estimation method using several spatial alternatives to the independent Fay-Herriot random effects model. Evaluation of the proposed method based on an application to estimation of four-person family median incomes for the U.S. states shows its usefulness. Limited but related simulation studies for the median incomes application reinforce our conclusion.AMS Subject Classification: 62F 15, 62D99
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2023-12-23T10:06:27Z
  DOI: 10.1177/00080683231199021
Small Area Estimation of Poverty and Wealth Using Geospatial Data: What
have We Learned So Far'
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  Authors: David Newhouse
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  This article offers a non-technical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predict poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improves the accuracy and efficiency of direct survey estimates. Models based on interpretable features appear to predict more accurately than estimates derived from convolutional neural networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models in common settings. Small area estimates using geospatial data can improve the design of social assistance programmes, particularly when the existing targeting system is poorly designed.AMS subject classification: 62P20
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2023-12-05T06:44:56Z
  DOI: 10.1177/00080683231198591
A Bayesian Semi-parametric Modelling Approach for Area Level Small Area
Studies
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  Authors: Marten Thompson, Snigdhansu Chatterjee
  Abstract: Calcutta Statistical Association Bulletin, Ahead of Print.
  We present a new semiparametric extension of the Fay-Herriot model, termed the agnostic Fay-Herriot model (AGFH), in which the sampling-level model is expressed in terms of an unknown general function [math]. Thus, the AGFH model can express any distribution in the sampling model since the choice of [math] is extremely broad. We propose a Bayesian modelling scheme for AGFH where the unknown function [math] is assigned a Gaussian Process prior. Using a Metropolis within Gibbs sampling Markov Chain Monte Carlo scheme, we study the performance of the AGFH model, along with that of a hierarchical Bayesian extension of the Fay-Herriot model. Our analysis shows that the AGFH is an excellent modelling alternative when the sampling distribution is non-Normal, especially in the case where the sampling distribution is bounded. It is also the best choice when the sampling variance is high. However, the hierarchical Bayesian framework and the traditional empirical Bayesian framework can be good modelling alternatives when the signal-to-noise ratio is high, and there are computational constraints.AMS subject classification: 62D05; 62F15
  Citation: Calcutta Statistical Association Bulletin
  PubDate: 2023-10-27T07:15:30Z
  DOI: 10.1177/00080683231198606

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