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Dissertations / Theses on the topic 'Latent Models, Small Area Estimation'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

BERTARELLI, GAIA. "LATENT MARKOV MODELS FOR AGGREGATE DATA: APPLICATION TO DISEASE MAPPING AND SMALL AREA ESTIMATION." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/96252.

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Latent Markov Models (LMMs) are a particular class of statistical models in which a latent process is assumed. LMMs allow for the analysis of longitudinal data when the response variables measure common characteristics of interest, which are not directly observable. In LMMs the characteristics of interest, and their evolution in time, are represented by a latent process that follows a first order discrete Markov chain and units are allowed to change latent state over time. In studying LMMs, it is important to distinguish between two components: the measurement model, i.e. the conditional dist

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2

Moura, Fernando Antonio da Silva. "Small area estimation using multilevel models." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241157.

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3

Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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4

Zhang, Qiong. "Small area quantile estimation under unit-level models." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62871.

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Sample surveys are widely used as a cost-effective way to collect information on variables of interest in target populations. In applications, we are generally interested in parameters such as population means, totals, and quantiles. Similar parameters for subpopulations or areas, formed by geographic areas and socio-demographic groups, are also of interest in applications. However, the sample size might be small or even zero in subpopulations due to the probability sampling and the budget limitation. There has been intensive research on how to produce reliable estimates for characteristics of

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5

Ganesh, Nadarajasundaram. "Small area estimation and prediction problems spatial models, Bayesian multiple comparisons and robust MSE estimation /." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/7241.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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6

Stukel, Diana M. (Diane Maria) Carleton University Dissertation Mathematics. "Small area estimation under one and two-fold nested error regression models." Ottawa, 1991.

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7

Wanjoya, Antony Kibira. "A Flexible Characterization of models for small area estimation: Theoretical developments and Applications." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421673.

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The demand for reliable small area estimates derived from survey data has increased greatly in recent years due to, among other things, their growing use in formulating policies and programs, allocation of government funds, regional planning, small area business decisions and other applications. Traditional area-specific (direct) estimates may not provide acceptable precision for small areas because sample sizes are seldom large enough in many small areas of interest. This makes it necessary to borrow information across related areas through indirect estimation based on models, using auxiliary

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8

Warnholz, Sebastian [Verfasser]. "Small Area Estimation Using Robust Extensions to Area Level Models : Theory, Implementation and Simulation Studies / Sebastian Warnholz." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1112553045/34.

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9

Yu, Mingyu Carleton University Dissertation Mathematics. "Nested-error regression models and small area estimation combining cross-sectional and time series data." Ottawa, 1993.

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10

Liu, Shiao. "Bayesian Analysis of Crime Survey Data with Nonresponse." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1175.

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Bayesian hierarchical models are effective tools for small area estimation by pooling small datasets together. The pooling procedures allow individual areas to “borrow strength” from each other to desirably improve the estimation. This work is an extension of Nandram and Choi (2002), NC, to perform inference on finite population proportions when there exists non-identifiability of the missing pattern for nonresponse in binary survey data. We review the small-area selection model (SSM) in NC which is able to incorporate the non-identifiability. Moreover, the proposed SSM, together with the indi

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11

Baldermann, Claudia [Verfasser]. "Robust Small Area Estimation under Spatial Non-Stationarity for Unit-Level Models : Theory and Empirical Results / Claudia Baldermann." Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1147758182/34.

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12

Ramesh, Sathya. "High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12188/.

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Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted r

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13

Yin, Jiani. "Bayesian Nonparametric Models for Multi-Stage Sample Surveys." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/197.

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It is a standard practice in small area estimation (SAE) to use a model-based approach to borrow information from neighboring areas or from areas with similar characteristics. However, survey data tend to have gaps, ties and outliers, and parametric models may be problematic because statistical inference is sensitive to parametric assumptions. We propose nonparametric hierarchical Bayesian models for multi-stage finite population sampling to robustify the inference and allow for heterogeneity, outliers, skewness, etc. Bayesian predictive inference for SAE is studied by embedding a parametric

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14

Articus, Charlotte [Verfasser], Ralf [Akademischer Betreuer] Münnich, and Ralf [Gutachter] Münnich. "Finite Mixture Models for Small Area Estimation in Cases of Unobserved Heterogeneity / Charlotte Articus ; Gutachter: Ralf Münnich ; Betreuer: Ralf Münnich." Trier : Universität Trier, 2018. http://d-nb.info/1197808175/34.

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15

Articus, Charlotte [Verfasser], Ralf T. [Akademischer Betreuer] Münnich, and Ralf [Gutachter] Münnich. "Finite Mixture Models for Small Area Estimation in Cases of Unobserved Heterogeneity / Charlotte Articus ; Gutachter: Ralf Münnich ; Betreuer: Ralf Münnich." Trier : Universität Trier, 2018. http://nbn-resolving.de/urn:nbn:de:hbz:385-1-9884.

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16

Manandhar, Binod. "Bayesian Models for the Analyzes of Noisy Responses From Small Areas: An Application to Poverty Estimation." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/188.

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We implement techniques of small area estimation (SAE) to study consumption, a welfare indicator, which is used to assess poverty in the 2003-2004 Nepal Living Standards Survey (NLSS-II) and the 2001 census. NLSS-II has detailed information of consumption, but it can give estimates only at stratum level or higher. While population variables are available for all households in the census, they do not include the information on consumption; the survey has the `population' variables nonetheless. We combine these two sets of data to provide estimates of poverty indicators (incidence, gap and sever

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17

Jahan, Farzana. "New insights into Bayesian models for spatial data." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212622/1/Farzana%20Jahan%20Thesis.pdf.

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Spatial data is often aggregated into small area. It is challenging to analyse these data. Two new statistical techniques were developed to tackle these challenges. Bayesian meta-analysis models were used to unveil geographic disparities in cancer using the Australian Cancer Atlas. More flexible Bayesian Empirical Likelihood models were used to analyse more complex data including COVID19 deaths. Both methods provided new insights into the analysis of spatial data.

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18

Erhardt, Erik Barry. "Bayesian Simultaneous Intervals for Small Areas: An Application to Mapping Mortality Rates in U.S. Health Service Areas." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0105104-195633/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: Poisson-Gamma Regression; MCMC; Bayesian; Small Area Estimation; Simultaneous Inference; Statistics Includes bibliographical references (p. 61-67).

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19

De, Moliner Anne. "Estimation robuste de courbes de consommmation électrique moyennes par sondage pour de petits domaines en présence de valeurs manquantes." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCK021/document.

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Dans cette thèse, nous nous intéressons à l'estimation robuste de courbes moyennes ou totales de consommation électrique par sondage en population finie, pour l'ensemble de la population ainsi que pour des petites sous-populations, en présence ou non de courbes partiellement inobservées.En effet, de nombreuses études réalisées dans le groupe EDF, que ce soit dans une optique commerciale ou de gestion du réseau de distribution par Enedis, se basent sur l'analyse de courbes de consommation électrique moyennes ou totales, pour différents groupes de clients partageant des caractéristiques communes

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20

Woo, Mi-Ja. "Robust estimation in mixture models and small area estimation using cross-sectional time series models." 2005. http://purl.galileo.usg.edu/uga%5Fetd/woo%5Fmi-ja%5F200508%5Fphd.

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21

Kramlinger, Peter. "Essays on Inference in Linear Mixed Models." Doctoral thesis, 2020. http://hdl.handle.net/21.11130/00-1735-0000-0005-1396-C.

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22

Savaþcý, Duygu. "Three studies on semi-mixed effects models." Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-000D-F1E3-3.

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