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Journal articles on the topic 'Latent Models, Small Area Estimation'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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1

Porter, Aaron T., Christopher K. Wikle, and Scott H. Holan. "Small Area Estimation via Multivariate Fay-Herriot Models with Latent Spatial Dependence." Australian & New Zealand Journal of Statistics 57, no. 1 (2015): 15–29. http://dx.doi.org/10.1111/anzs.12101.

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2

Choi, Jungsoon, and Andrew B. Lawson. "Bayesian spatially dependent variable selection for small area health modeling." Statistical Methods in Medical Research 27, no. 1 (2016): 234–49. http://dx.doi.org/10.1177/0962280215627184.

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Statistical methods for spatial health data to identify the significant covariates associated with the health outcomes are of critical importance. Most studies have developed variable selection approaches in which the covariates included appear within the spatial domain and their effects are fixed across space. However, the impact of covariates on health outcomes may change across space and ignoring this behavior in spatial epidemiology may cause the wrong interpretation of the relations. Thus, the development of a statistical framework for spatial variable selection is important to allow for

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3

Badru, Jamiu O., and Kamilu A. Saka. "Structural Equation Modeling Analysis of Equity-Based Islamic Financing for Nigerian Small-Scale Enterprises Growth." American Journal of Applied Statistics and Economics 4, no. 1 (2025): 32–40. https://doi.org/10.54536/ajase.v4i1.4424.

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This study investigates within Covariance-based Structural Equation Modeling (CV-SEM) the potential causal relationships between Islamic equity-based financing and Nigerian Small Scale Enterprises (SSEs) expansion. A quantitative survey design was employed to collect questionnaire-based, cross-sectional primary data from 512 educated SSE owners and managers in eight business districts in the Abeokuta metropolis. STATA 12.1 software was utilized to estimate CV-SEM with the maximum likelihood method. Intuitively, the Principal Component Analysis (PCA) estimator was applied to derive continuous d

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4

Bekalo, Daniel Biftu, Anthony Kibira Wanjoya, and Samuel Musili Mwalili. "Bayesian rank likelihood-based estimation: An application to low birth weight in Ethiopia." PLOS ONE 19, no. 5 (2024): e0303637. http://dx.doi.org/10.1371/journal.pone.0303637.

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Background Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability. Methods This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk

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5

Marcq, S., and J. Weiss. "Influence of leads widths distribution on turbulent heat transfer between the ocean and the atmosphere." Cryosphere Discussions 5, no. 5 (2011): 2765–97. http://dx.doi.org/10.5194/tcd-5-2765-2011.

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Abstract. Leads are linear-like structures of open water within the sea ice cover that develop as the result of fracturing due to divergence or shear. Through leads, air and water come into contact and directly exchange latent and sensible heat through convective processes driven by the large temperature and moisture differences between them. In the central Arctic, leads only cover 1 to 2% of the ocean during winter, but account for more than 80% of the heat fluxes. Furthermore, narrow leads (several meters) are more than twice as efficient at transmitting turbulent heat than larger ones (seve

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6

Marcq, S., and J. Weiss. "Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere." Cryosphere 6, no. 1 (2012): 143–56. http://dx.doi.org/10.5194/tc-6-143-2012.

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Abstract. Leads are linear-like structures of open water within the sea ice cover that develop as the result of fracturing due to divergence or shear. Through leads, air and water come into contact and directly exchange latent and sensible heat through convective processes driven by the large temperature and moisture differences between them. In the central Arctic, leads only cover 1 to 2% of the ocean during winter, but account for more than 70% of the upward heat fluxes. Furthermore, narrow leads (several meters) are more than twice as efficient at transmitting turbulent heat than larger one

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7

Nassar, Ayman, Alfonso Torres-Rua, Lawrence Hipps, et al. "Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem." Remote Sensing 14, no. 2 (2022): 372. http://dx.doi.org/10.3390/rs14020372.

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Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in

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8

Babel, W., S. Huneke, and T. Foken. "A framework to utilize turbulent flux measurements for mesoscale models and remote sensing applications." Hydrology and Earth System Sciences Discussions 8, no. 3 (2011): 5165–225. http://dx.doi.org/10.5194/hessd-8-5165-2011.

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Abstract. Meteorologically measured fluxes of energy and matter between the surface and the atmosphere originate from a source area of certain extent, located in the upwind sector of the device. The spatial representativeness of such measurements is strongly influenced by the heterogeneity of the landscape. The footprint concept is capable of linking observed data with spatial heterogeneity. This study aims at upscaling eddy covariance derived fluxes to a grid size of 1 km edge length, which is typical for mesoscale models or low resolution remote sensing data. Here an upscaling strategy is pr

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9

Klees, R., E. A. Zapreeva, H. C. Winsemius, and H. H. G. Savenije. "The bias in GRACE estimates of continental water storage variations." Hydrology and Earth System Sciences Discussions 3, no. 6 (2006): 3557–94. http://dx.doi.org/10.5194/hessd-3-3557-2006.

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Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE err

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10

Kubokawa, Tatsuya. "Linear Mixed Models and Small Area Estimation." Japanese journal of applied statistics 35, no. 3 (2006): 139–61. http://dx.doi.org/10.5023/jappstat.35.139.

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11

Dagne, Getachew A. "Bayesian transformed models for small area estimation." Test 10, no. 2 (2001): 375–91. http://dx.doi.org/10.1007/bf02595703.

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12

Benedetti, Roberto, Monica Pratesi, and Nicola Salvati. "Local stationarity in small area estimation models." Statistical Methods & Applications 22, no. 1 (2012): 81–95. http://dx.doi.org/10.1007/s10260-012-0208-1.

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13

Chambers, Ray, and Nikos Tzavidis. "M-quantile models for small area estimation." Biometrika 93, no. 2 (2006): 255–68. http://dx.doi.org/10.1093/biomet/93.2.255.

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14

Ghosh, Malay, Kannan Natarajan, T. W. F. Stroud, and Bradley P. Carlin. "Generalized Linear Models for Small-Area Estimation." Journal of the American Statistical Association 93, no. 441 (1998): 273–82. http://dx.doi.org/10.1080/01621459.1998.10474108.

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15

Ferraz, V. R. S., and F. A. S. Moura. "Small area estimation using skew normal models." Computational Statistics & Data Analysis 56, no. 10 (2012): 2864–74. http://dx.doi.org/10.1016/j.csda.2011.07.005.

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16

Moretti, Angelo, Natalie Shlomo, and Joseph W. Sakshaug. "Multivariate Small Area Estimation of Multidimensional Latent Economic Well‐being Indicators." International Statistical Review 88, no. 1 (2020): 1–28. http://dx.doi.org/10.1111/insr.12333.

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17

Klees, R., E. A. Zapreeva, H. C. Winsemius, and H. H. G. Savenije. "The bias in GRACE estimates of continental water storage variations." Hydrology and Earth System Sciences 11, no. 4 (2007): 1227–41. http://dx.doi.org/10.5194/hess-11-1227-2007.

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Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE err

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18

Berg, Emily. "Construction of Databases for Small Area Estimation." Journal of Official Statistics 38, no. 3 (2022): 673–708. http://dx.doi.org/10.2478/jos-2022-0031.

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Abstract The demand for small area estimates can conflict with the objective of producing a multi-purpose data set. We use donor imputation to construct a database that supports small area estimation. Appropriately weighted sums of observed and imputed values produce model-based small area estimates. We develop imputation procedures for both unit-level and area-level models. For area-level models, we restrict to linear models. We assume a single vector of covariates is used for a possibly multivariate response. Each record in the imputed data set has complete data, an estimation weight, and a

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19

Zhang, Junni L., and John Bryant. "Fully Bayesian Benchmarking of Small Area Estimation Models." Journal of Official Statistics 36, no. 1 (2020): 197–223. http://dx.doi.org/10.2478/jos-2020-0010.

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AbstractEstimates for small areas defined by social, demographic, and geographic variables are increasingly important for official statistics. To overcome problems of small sample sizes, statisticians usually derive model-based estimates. When aggregated, however, the model- based estimates typically do not agree with aggregate estimates (benchmarks) obtained through more direct methods. This lack of agreement between estimates can be problematic for users of small area estimates. Benchmarking methods have been widely used to enforce agreement. Fully Bayesian benchmarking methods, in the sense

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20

Hobza, Tomáš, and Domingo Morales. "Small area estimation under random regression coefficient models." Journal of Statistical Computation and Simulation 83, no. 11 (2012): 2160–77. http://dx.doi.org/10.1080/00949655.2012.684094.

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21

Logan, John R., Cici Bauer, Jun Ke, Hongwei Xu, and Fan Li. "Models for Small Area Estimation for Census Tracts." Geographical Analysis 52, no. 3 (2019): 325–50. http://dx.doi.org/10.1111/gean.12215.

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22

Salvati, Nicola, Monica Pratesi, Nikos Tzavidis, and Ray Chambers. "SPATIAL M-QUANTILE MODELS FOR SMALL AREA ESTIMATION." Statistics in Transition new series 10, no. 2 (2009): 251–67. http://dx.doi.org/10.59170/stattrans-2009-019.

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In small area estimation direct survey estimates that rely only on area-specific data can exhibit large sampling variability due to small sample sizes at the small area level. Efficient small area estimates can be constructed using explicit linking models that borrow information from related areas. The most popular class of models for this purpose are models that include random area effects. Estimation for these models typically assumes that the random area effects are uncorrelated. In many situations, however, it is reasonable to assume that the effects of neighbouring areas are correlated. M

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23

Benavent, Roberto, and Domingo Morales. "Multivariate Fay–Herriot models for small area estimation." Computational Statistics & Data Analysis 94 (February 2016): 372–90. http://dx.doi.org/10.1016/j.csda.2015.07.013.

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24

Sugasawa, Shonosuke, and Tatsuya Kubokawa. "Small area estimation with mixed models: a review." Japanese Journal of Statistics and Data Science 3, no. 2 (2020): 693–720. http://dx.doi.org/10.1007/s42081-020-00076-x.

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25

Fabrizi, Enrico, and Carlo Trivisano. "Robust linear mixed models for Small Area Estimation." Journal of Statistical Planning and Inference 140, no. 2 (2010): 433–43. http://dx.doi.org/10.1016/j.jspi.2009.07.022.

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26

von Rosen, Tatjana, and Dietrich von Rosen. "Small area estimation using reduced rank regression models." Communications in Statistics - Theory and Methods 49, no. 13 (2019): 3286–97. http://dx.doi.org/10.1080/03610926.2019.1586946.

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27

Hoshino, Tadao. "SEMIPARAMETRIC ESTIMATION OF CENSORED SPATIAL AUTOREGRESSIVE MODELS." Econometric Theory 36, no. 1 (2019): 48–85. http://dx.doi.org/10.1017/s0266466618000488.

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This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric, in the sense that the error distribution is not parametrically specified and can be heteroskedastic. Under a median restriction, we show that the proposed estimator is consistent and asymptotically normally distributed. As an empirical illustration, we investigate the determinants of the risk of assault and other violent crimes including injury in

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28

Qiu, Xiang, Qinchun Ke, Xueqin Zhou, and Yulu Liu. "Small Area Estimation under Poisson–Dirichlet Process Mixture Models." Axioms 13, no. 7 (2024): 432. http://dx.doi.org/10.3390/axioms13070432.

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In this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the parameter estimation using the Empirical Bayesian(EB) estimation method, and we adopt various methods such as the Maximum Likelihood Estimation(MLE) method and the Markov chain Monte Carlo algorithm to solve the model parameter estimation jointly. The viability of the model is verified using numerical simulation, and the proposed model is applied to an

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29

Young, Linda J., and Lu Chen. "Using Small Area Estimation to Produce Official Statistics." Stats 5, no. 3 (2022): 881–97. http://dx.doi.org/10.3390/stats5030051.

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The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estima

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30

Choi, Jungsoon, Andrew B. Lawson, Bo Cai, and Md Monir Hossain. "Evaluation of Bayesian spatiotemporal latent models in small area health data." Environmetrics 22, no. 8 (2011): 1008–22. http://dx.doi.org/10.1002/env.1127.

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31

Nekrašaite-Liege, Vilma. "Some applications of panel data models in small area estimation." Statistics in Transition new series 12, no. 2 (2011): 265–80. http://dx.doi.org/10.59170/stattrans-2011-017.

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This study uses a real population from Statistics Lithuania to investigate the performance of different types of estimation strategies. The estimation strategy is a combination of sampling design and estimation design. The sampling designs include equal probability design (SRS) and unequal probability designs (stratified SRS and model-based sampling designs). Design-based direct Horvitz-Thompson, indirect model-assisted GREG estimator and indirect model-based estimator are used to estimate the totals in small area estimation. The underlying panel-type models (linear fixed-effects type or linea

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32

Pusponegoro, Novi Hidayat, Anik Djuraidah, Anwar Fitrianto, and I. Made Sumertajaya. "Geo-additive Models in Small Area Estimation of Poverty." Journal of Data Science and Its Applications 2, no. 1 (2019): 59–67. http://dx.doi.org/10.21108/jdsa.2019.2.15.

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Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model r

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33

Torkashvand, Elaheh, Mohammad Jafari Jozani, and Mahmoud Torabi. "Clustering in small area estimation with area level linear mixed models." Journal of the Royal Statistical Society: Series A (Statistics in Society) 180, no. 4 (2017): 1253–79. http://dx.doi.org/10.1111/rssa.12308.

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34

Esteban, María Dolores, María José Lombardía, Esther López-Vizcaíno, Domingo Morales, and Agustín Pérez. "Small area estimation of proportions under area-level compositional mixed models." TEST 29, no. 3 (2019): 793–818. http://dx.doi.org/10.1007/s11749-019-00688-w.

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35

Esteban, M. D., D. Morales, A. Pérez, and L. Santamaría. "Small area estimation of poverty proportions under area-level time models." Computational Statistics & Data Analysis 56, no. 10 (2012): 2840–55. http://dx.doi.org/10.1016/j.csda.2011.10.015.

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36

Jiang, Jiming, and J. Sunil Rao. "Robust Small Area Estimation: An Overview." Annual Review of Statistics and Its Application 7, no. 1 (2020): 337–60. http://dx.doi.org/10.1146/annurev-statistics-031219-041212.

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A small area typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE). We survey this work and provide a comprehensive review here. We begin with a brief review of the traditional SAE methods. We then discuss SAE methods that are developed under weaker assumptions and SAE metho

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37

You, Yong, and J. N. K. Rao. "Small area estimation using unmatched sampling and linking models." Canadian Journal of Statistics 30, no. 1 (2002): 3–15. http://dx.doi.org/10.2307/3315862.

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38

Rai, Anil, N. K. Gupta, and Randhir Singh. "Small area estimation of crop production using spatial models." Model Assisted Statistics and Applications 2, no. 2 (2007): 89–98. http://dx.doi.org/10.3233/mas-2007-2204.

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39

Moura, F. AS, and H. S. Migon. "Bayesian spatial models for small area estimation of proportions." Statistical Modelling: An International Journal 2, no. 3 (2002): 183–201. http://dx.doi.org/10.1191/1471082x02st032oa.

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40

Maiti, Tapabrata. "Robust generalized linear mixed models for small area estimation." Journal of Statistical Planning and Inference 98, no. 1-2 (2001): 225–38. http://dx.doi.org/10.1016/s0378-3758(00)00302-5.

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41

Sugasawa, Shonosuke, Tatsuya Kubokawa, and J. N. K. Rao. "Small area estimation via unmatched sampling and linking models." TEST 27, no. 2 (2017): 407–27. http://dx.doi.org/10.1007/s11749-017-0551-5.

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42

Sinha, Sanjoy K. "Robust small area estimation in generalized linear mixed models." METRON 77, no. 3 (2019): 201–25. http://dx.doi.org/10.1007/s40300-019-00161-6.

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43

Marhuenda, Yolanda, Isabel Molina, and Domingo Morales. "Small area estimation with spatio-temporal Fay–Herriot models." Computational Statistics & Data Analysis 58 (February 2013): 308–25. http://dx.doi.org/10.1016/j.csda.2012.09.002.

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44

Rao, Jon N. K., Sanjoy K. Sinha, and Laura Dumitrescu. "Robust small area estimation under semi-parametric mixed models." Canadian Journal of Statistics 42, no. 1 (2013): 126–41. http://dx.doi.org/10.1002/cjs.11199.

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45

Torabi, Mahmoud. "Spatial generalized linear mixed models in small area estimation." Canadian Journal of Statistics 47, no. 3 (2019): 426–37. http://dx.doi.org/10.1002/cjs.11502.

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46

Li, Huapeng, Yukun Liu, and Riquan Zhang. "Small area estimation under transformed nested-error regression models." Statistical Papers 60, no. 4 (2017): 1397–418. http://dx.doi.org/10.1007/s00362-017-0879-7.

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47

Linzer, Drew A. "Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators." Political Analysis 19, no. 2 (2011): 173–87. http://dx.doi.org/10.1093/pan/mpr006.

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Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities an

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48

González-Manteiga, W., MJ Lombarda, I. Molina, D. Morales, and L. Santamaría. "Small area estimation under Fay–Herriot models with non-parametric estimation of heteroscedasticity." Statistical Modelling: An International Journal 10, no. 2 (2010): 215–39. http://dx.doi.org/10.1177/1471082x0801000206.

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49

Sugasawa, Shonosuke, and Tatsuya Kubokawa. "Correction to: Small area estimation with mixed models: a review." Japanese Journal of Statistics and Data Science 4, no. 1 (2021): 477. http://dx.doi.org/10.1007/s42081-021-00108-0.

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50

Jeong, Seok-Oh, and Key-Il Shin. "Semiparametric and Nonparametric Mixed Effects Models for Small Area Estimation." Korean Journal of Applied Statistics 26, no. 1 (2013): 71–79. http://dx.doi.org/10.5351/kjas.2013.26.1.071.

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