Se connecter

Bibliographies thématiques / Latent Models, Small Area Estimation / Articles de revues

Pour voir les autres types de publications sur ce sujet consultez le lien suivant : Latent Models, Small Area Estimation.

Articles de revues sur le sujet « Latent Models, Small Area Estimation »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres

Choisissez une source :

Consultez les 50 meilleurs articles de revues pour votre recherche sur le sujet « Latent Models, Small Area Estimation ».

À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.

Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.

Parcourez les articles de revues sur diverses disciplines et organisez correctement votre bibliographie.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 the estimation of the space-varying patterns of covariate effects as well as the early detection of disease over space. In this paper, we develop flexible spatial variable selection approaches to find the spatially-varying subsets of covariates with significant effects. A Bayesian hierarchical latent model framework is applied to account for spatially-varying covariate effects. We present a simulation example to examine the performance of the proposed models with the competing models. We apply our models to a county-level low birth weight incidence dataset in Georgia.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 data scores for the latent variables. The fit indices tested (RMSEA, CFI and TLI) indicate that the study structural models fit the observed data. From the CV-SEM estimations, musharakah and mudharabah have potentially positive and significant impacts on the sales growth of SSEs in the study area. Findings reveal further that Islamic equity financing tools such as musharakah, diminishing musharakah, and mudharabah are more likely to enhance the development of SSEs in the Abeokuta metropolis significantly and indirectly through the Islamic finance legal framework. The study affirms that the expansion of small enterprises in the study area will be directly, positively, and significantly influenced by Islamic shared finance (Musharakah) and Islamic joint partnership (Mudharabah). However, the indirect positive and significant impact of Islamic equity finance through a legal framework (mediating factor) is stronger for the development of small firms than the direct impact. The study advocates that the federal government of Nigeria should institute a suitable legal framework for Islamic equity financing to enhance the operational capacity of small-scale businesses.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 factors in Ethiopia. Data from the Ethiopian Demographic and Health Survey (EDHS) of 2016 were used as a data source for the study. Data stratified all regions into urban and rural areas. Among 15, 680 representative selected households, the analysis included complete cases from 10, 641 children (0-59 months). The evaluation of model performance considered metrics such as the root mean square error, the mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. Results Based on the values of root mean square error, mean absolute error, and probability coverage, the estimates obtained from the proposed model outperform the classical estimates. According to the result, 40.92% of the children were born with low birth weight. The study also found that low birth weight is unevenly distributed across different regions of the country with the highest amounts of variation observed in the Afar, Somali and Southern Nations, Nationalities, and Peoples regions as represented by the latent trait parameter of the model. In contrast, the lowest low birth weight variation was recorded in the Addis Ababa, Dire Dawa, and Amhara regions. Furthermore, there were significant associations between birth weight and several factors, including the age of the mother, number of antenatal care visits, order of birth and the body mass index as indicated by the average posterior beta values of (β1= -0.269, CI=-0.320, -0.220), (β2= -0.235, CI=-0.268, -0.202), (β3= -0.120, CI=-0.162, -0.074) and (β5= -0.257, CI=-0.291, -0.225). Conclusions The study showed that the low birth weight estimates obtained from the latent trait model outperform the classical estimates. The study also revealed that the prevalence of low birth weight varies between different regions of the country, indicating the need for targeted interventions in areas with a higher prevalence. To effectively reduce the prevalence of low birth weight and improve maternal and child health outcomes, it is important to concentrate efforts on regions with a higher burden of low birth weight. This will help implement interventions that are tailored to the unique challenges and needs of each area. Health institutions should take measures to reduce low birth weight, with a special focus on the factors identified in this study.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 (several hundreds of meters). We show that lead widths are power law distributed, P(X)~X−a with a>1, down to very small spatial scales (20 m or below). This implies that the open water fraction is by far dominated by very small leads. Using two classical formulations, which provide first order turbulence closure for the fetch-dependence of heat fluxes, we find that the mean heat fluxes (sensible and latent) over open water are up to 55 % larger when considering the lead width distribution obtained from a SPOT satellite image of the ice cover, compared to the situation where the open water fraction constitutes one unique large lead and the rest of the area is covered by ice, as it is usually considered in climate models at the grid scale. This difference may be even larger if we assume that the power law scaling of lead widths extents down to smaller (~1 m) scales. Such estimations may be a first step towards a subgrid scale parameterization of the spatial distribution of open water for heat fluxes calculations in ocean/sea ice coupled models.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 ones (several hundreds of meters). We show that lead widths are power law distributed, P(X)~X−a with a>1, down to very small spatial scales (20 m or below). This implies that the open water fraction is by far dominated by very small leads. Using two classical formulations, which provide first order turbulence closure for the fetch-dependence of heat fluxes, we find that the mean heat fluxes (sensible and latent) over open water are up to 55% larger when considering the lead-width distribution obtained from a SPOT satellite image of the ice cover, compared to the situation where the open water fraction constitutes one unique large lead and the rest of the area is covered by ice, as it is usually considered in climate models at the grid scale. This difference may be even larger if we assume that the power law scaling of lead widths extends down to smaller (~1 m) scales. Such estimations may be a first step towards a subgrid scale parameterization of the spatial distribution of open water for heat fluxes calculations in ocean/sea ice coupled models.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 presented, utilizing footprint modelling and SVAT modelling as well as observations from a target land-use area. The general idea of this scheme is to model fluxes from adjacent land-use types and combine them with the measured flux data to yield a grid representative flux according to the land-use distribution within the grid cell. The performance of the upscaling routine is evaluated with real datasets, which are considered to be land-use specific fluxes in a grid cell. The measurements above rye and maize fields stem from the LITFASS experiment 2003 in Lindenberg, Germany and the respective modelled timeseries were derived by the SVAT model SEWAB. Contributions from each land-use type to the observations are estimated using a forward lagrangian stochastic model. A representation error is defined as the error in flux estimates made when accepting the measurements unchanged as grid representative flux and ignoring flux contributions from other land-use types within the respective grid cell. Results show that this representation error can be reduced up to 56 % when applying the spatial integration. This shows the potential for further application of this strategy, although the absolute differences between flux observations from rye and maize were so small, that the spatial integration would be rejected in a real situation. Corresponding thresholds for this decision have been estimated as a minimum mean absolute deviation in modelled timeseries of the different land-use types with 35 W m−2 for the sensible heat flux and 50 W m−2 for the latent heat flux. Finally, a quality lagging scheme to classify the data with respect to representativeness for a given grid cell is proposed, based on an overall flux error estimate. This enables the data user to infer the uncertainty of mesoscale models and remote sensing products with respect to ground observations. Major uncertainty sources remaining are the lack of an adequate method for energy balance closure correction as well as model structure and parameter estimation, when applying the model for surfaces without flux measurements.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations and that bias correction using existing hydrological models significantly improves the quality of GRACE estimates. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. It is shown that after bias correction, the fit between GRACE and a hydrological model is overoptimistic, if the same hydrological model is used to estimate the bias and to compare with GRACE. If another hydrological model is used to compute the bias, the fit is less, although the improvement is still very significant compared with uncorrected GRACE estimates of water storage variations. Therefore, the proposed approach for bias correction works for the target areas subject to this study. It may also be an option for other target areas provided that some reasonable a priori information about water storage variations are available.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 set of replicate weights for mean square error (MSE) estimation. We compare imputation procedures based on area-level models to those based on unit-level models through simulation. We apply the methods to the Iowa Seat-Belt Use Survey, a survey designed to produce state-level estimates of the proportions of vehicle occupants who wear a seat-belt. We develop a bivariate unit-level model for prediction of county-level proportions of belted drivers and total occupants. We impute values for the proportions of belted drivers and vehicle occupants onto the full population of road segments in the sampling frame. The resulting imputed data set returns approximations for the county-level predictors based on the bivariate model.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 of yielding full posterior distributions after benchmarking, can provide coherent measures of uncertainty for all quantities of interest, but research on fully Bayesian benchmarking methods is limited. We present a flexible fully Bayesian approach to benchmarking that allows for a wide range of models and benchmarks. We revise the likelihood by multiplying it by a probability distribution that measures agreement with the benchmarks. We outline Markov chain Monte Carlo methods to generate samples from benchmarked posterior distributions. We present two simulations, and an application to English and Welsh life expectancies.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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. Models that extend conventional random effects models to account for spatial correlation between the small areas have been recently proposed in literature. A new semi-parametric approach to small area estimation is based on the use of M-quantile models. Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumptions and are robust to the presence of outliers. In its current form, however, the M-quantile approach to small area estimation does not allow for spatially correlated area effects. The aim of this paper is to extend the M-quantile approach to account for such spatial correlation between small areas.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 the Tokyo metropolitan area.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 actual small area estimation problem. Compared to the conventional normal random effects linear model, the proposed model is more accurate for the estimation of complex real-world application data, which makes it suitable for a broader range of application contexts.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 linear random-effects type) are examined in both stages of estimation strategies: sample design and construction of estimators.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 methods that are robust in certain ways, such as in terms of outliers or model failure. Our discussion also includes topics such as nonparametric SAE methods, Bayesian approaches, model selection and diagnostics, and missing data. A brief review of software packages available for implementing robust SAE methods is also given.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

Nous offrons des réductions sur tous les plans premium pour les auteurs dont les œuvres sont incluses dans des sélections littéraires thématiques. Contactez-nous pour obtenir un code promo unique!