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Articles de revues sur le sujet "Latent Models, Small Area Estimation"
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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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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 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.
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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 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.
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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 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.
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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 (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.
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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 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.
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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 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.
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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 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.
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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 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.
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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 intégralThèses sur le sujet "Latent Models, Small Area Estimation"
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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 distribution of the response variables given the latent process, and the latent model, i.e. the distribution of the latent process. This thesis focuses on LMMs for aggregated data. It considers two fields of applications: disease mapping and small area estimation.
The goal of disease mapping is the study of the geographical pattern and variation of a disease measured through counts and incidence rates. From a methodological point of view, this work extends LMMs to include a spatial pattern in the latent model. This extension allows the probability of being in a latent state and the probability to move from a latent state to another over time to be influenced by the neighbouring areas. The model is fitted within a Bayesian framework using Gibbs and Random Metropolis Hastings algorithms with augmented data that allows for a more efficient sampling of model parameters. Simulations studies are also conducted to investigate the performance of the proposed model on data generated under different settings. The model has also been applied to a data set of county specific lung cancer deaths counts in the state of Ohio, USA, during the years 1968-1988.
Small area estimation (SAE) methods are used in inference for finite populations to obtain estimates of parameters of interest when domain sample sizes are too small to provide adequate precision for direct domain estimators. The second work develops a new area-level SAE method using LMMs. In particular, since area-level SAE models consider a sampling and a linking model, a LMM is used as the linking model. In a hierarchical Bayesian framework the sampling model is introduced as the highest level of hierarchy. In this context, data are considered aggregated because direct estimates are usually mean and frequencies. Under the assumption of normality the response variable, the model is estimated using a Gibbs sampling in a data augmentation context. The application field in this second work is particularly relevant: it uses yearly unemployment rates at Local Labour Market Areas level for the period 2004-2014 from the Labour Force Survey conducted by the Italian National Statistical Institute (ISTAT).
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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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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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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 interest for subpopulations for which the sample size is small or even zero. We call this line of research Small Area Estimation (SAE). In this thesis, we study the performance of a number of small area quantile estimators based on a popular unit-level model and its variations. When a finite population can be regarded as a sample from some model, we may use the whole sample from the finite population to determine the model structure with a good precision. The information can then be used to produce more reliable estimates for small areas. However, if the model assumption is wrong, the resulting estimates can be misleading and their mean squared errors can be underestimated. Therefore, it is critical to check the robustness of estimators under various model mis-specification scenarios. In this thesis, we first conduct simulation studies to investigate the performance of three small area quantile estimators in the literature. They are found not to be very robust in some likely situations. Based on these observations, we propose an approach to obtain more robust small area quantile estimators. Simulation results show that the proposed new methods have superior performance either when the error distribution in the model is non-normal or the data set contain many outliers.<br>Science, Faculty of<br>Statistics, Department of<br>Graduate
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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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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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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 information such as recent census data and current administrative data. Methods based on models are now widely accepted. The principal focus of this thesis is the development of a flexible modeling strategy in small area estimation with demonstrations and evaluations using the 1989 United States census bureau median income dataset.
This dissertation is divided into two main parts, the first part deals with development of the proposed model and comparision of this model to the standard area-level Fay-Herriot model through the empirical Bayes (EB) approach. Results from these two models are compared in terms of average relative bias, average squared relative bias, average absolute bias, average squared deviation as well as the empirical mean square error. The proposed model exhibits remarkably better performance over the standard Fay-Herriot model.
The second part represents our attempt to construct a hierarchical Bayes (HB) approach to estimate parameters for the proposed model, with implementation carried out by Markov chain Monte Carlo (MCMC) techniques. MCMC was implemented via the Gibbs sampling algorithm using R software package. We used several graphical tools to assess convergence and determine the length of the burn-in period. Results from the two models are compared in terms of average relative bias, average squared relative bias and average absolute bias. Our empirical results highlight the superiority of using the proposed model over the Fay-Herriot model. However, the advantage of the proposed model comes at a price since its implementation is mildly more difficult than the Fay-Herriot model.<br>L'esigenza di stime affidabili per piccole aree tratte da sondaggi è cresciuta notevolmente negli ultimi anni, grazie all'aumento del loro utilizzo nella formulazione delle politiche, nella ripartizione dei fondi statali, nella pianificazione regionale, nelle applicazioni business e in altre applicazioni. Le tradizionali stime specifiche per l'area (stime dirette) potrebbero non fornire una precisione accettabile, perché la numerosità campionaria in molte delle piccole aree d'interesse potrebbe essere ridotta o nulla. Questo rende neccessario sfruttare le informazioni dalle zone simili, tramitte una stima indiretta basata sui modelli per informazioni ausiliarie come i dati dei censimenti o i dati amministrativi. I metodi basati sui modelli sono ora piuttosto diffusi. L'attenzione principale di questa tesi è sviluppare una strategia di modellazione flessibile nella stima di piccole aree, e la sua valutazione utilizzando il Censimento negli Stati Uniti sul reddito mediano, del 1989.
Questa dissertazione è composta di due parti : la prima tratta lo sviluppo del modello e il confronto del modello proposto con il modello standard di Fay-Herriot tramite l'approcio di Bayes empirico. I risultati per questi due modelli sono stati confrontati in termini del bias relativo medio, del bias quadratico medio, del bias medio assoluto, della deviazione quadratica media ed inotre in termini del errore quadratico medio empirico. Il modello proposto dimostra un rendimento assai migliore rispetto al modello standard di Fay-Herriot.
La seconda parte presenta il nostro tentativo di costruire un approccio di Bayes Gerarchico per la stima dei parametri del modello proposto, con l'attuazione delle tecniche di Markov Chain Monte Carlo (MCMC). MCMC è stato utilizzato tramitte l'algoritmo di campionamento Gibbs, utilizzando il software R. I risultati dai due modelli sono stati confrontati in termini di bias relativo medio, bias relativo quadratico medio e il bias assoluto medio. I nostri risultati empirici sottolineano la superiorità del modello proposto rispetto al modello Fay-Herriot. Tuttavia, il vantaggio del modello proposto è limitato visto che la sua attuazione è leggermente più complicata rispetto al modello di Fay-Herriot.
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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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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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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 individual-area selection model (ISM), and the small-area pattern-mixture model (SPM) are evaluated by real crime data in Stasny (1991). Furthermore, the methodology is compared to ISM and SPM using simulated small area datasets. Computational issues related to the MCMC are also discussed.
Livres sur le sujet "Latent Models, Small Area Estimation"
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Sugasawa, Shonosuke, and Tatsuya Kubokawa. Mixed-Effects Models and Small Area Estimation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9486-9.
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Morales, Domingo, María Dolores Esteban, Agustín Pérez, and Tomáš Hobza. A Course on Small Area Estimation and Mixed Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63757-6.
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Sugasawa, Shonosuke. Mixed-Effects Models and Small Area Estimation. Springer, 2023.
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Pratesi, Monica. Analysis of Poverty Data by Small Area Estimation. Wiley & Sons, Incorporated, John, 2016.
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Pratesi, Monica. Analysis of Poverty Data by Small Area Estimation. Wiley & Sons, Incorporated, John, 2015.
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Analysis of poverty data by small area estimation. John Wiley & Sons, 2016.
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Pratesi, Monica. Analysis of Poverty Data by Small Area Estimation. Wiley & Sons, Incorporated, John, 2015.
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Pratesi, Monica. Analysis of Poverty Data by Small Area Estimation. Wiley & Sons, Limited, John, 2016.
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Course on Small Area Estimation and Mixed Models: Methods, Theory and Applications in R. Springer International Publishing AG, 2022.
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Morales, Domingo, María Dolores Esteban, Agustín Pérez, and Tomás Hobza. Course on Small Area Estimation and Mixed Models: Methods, Theory and Applications in R. Springer International Publishing AG, 2021.
Trouver le texte intégralChapitres de livres sur le sujet "Latent Models, Small Area Estimation"
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Longford, Nicholas T. "Small-area estimation." In Models for Uncertainty in Educational Testing. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4613-8463-2_8.
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Morales, Domingo, María Dolores Esteban, Agustín Pérez, and Tomáš Hobza. "Small Area Estimation." In A Course on Small Area Estimation and Mixed Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63757-6_1.
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Jiang, Jiming, and Sunil J. Rao. "More Flexible Models." In Robust Small Area Estimation. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003395171-5.
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Haughton, Dominique, and Jonathan Haughton. "Multilevel Models and Small-Area Estimation." In Living Standards Analytics. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0385-2_13.
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Esteban, María Dolores, Domingo Morales, and Agustín Pérez. "Area-level Spatio-temporal Small Area Estimation Models." In Analysis of Poverty Data by Small Area Estimation. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118814963.ch11.
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Sugasawa, Shonosuke, and Tatsuya Kubokawa. "Extensions of Basic Small Area Models." In Mixed-Effects Models and Small Area Estimation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9486-9_8.
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Sugasawa, Shonosuke, and Tatsuya Kubokawa. "Advanced Theory of Basic Small Area Models." In Mixed-Effects Models and Small Area Estimation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9486-9_6.
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Morales, Domingo, María Dolores Esteban, Agustín Pérez, and Tomáš Hobza. "Area-Level Poisson Mixed Models." In A Course on Small Area Estimation and Mixed Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63757-6_20.
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Bocci, Chiara, and Alessandra Petrucci. "Spatial Information and Geoadditive Small Area Models." In Analysis of Poverty Data by Small Area Estimation. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118814963.ch13.
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Sugasawa, Shonosuke, and Tatsuya Kubokawa. "General Mixed-Effects Models and BLUP." In Mixed-Effects Models and Small Area Estimation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9486-9_2.
Texte intégralActes de conférences sur le sujet "Latent Models, Small Area Estimation"
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He, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, and Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.
Texte intégralRésumé :
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.
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Tanabe, Yuji, and Hiromitsu Shimakawa. "Assessing Engagement of the Elderly in Active Listening from Body Movement." In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004022.
Texte intégralRésumé :
This study propose s a method for estimating the conversational state in active listening using voice and body movement data to facilitate for participants to exchange their words. In recent years, the problem of apathy among the elderly has run into a serious problem as the population ages. Active listening, a type of counseling technique, is useful to address the problem. In active listening, to activate the elderly person, a listener should do nothing other than listen to what the elderly person talks. However, it is difficult for the elderly person to willingly talk because they are not familiar enough with each other to make conversation in a frank mode. In the study, the body movement of the elderly person is recorded as well as the voice of both participants. Hidden Markov models , to which those data are fed, estimate the latent conversational state during active listening. A random forest models are constructed to examine the importance of each variables fed to the hidden Markov model.To test the usefulness of the proposed method, a one-on-one listening experiment is conducted between a listener and a speaker. The difference in the body movement derives two personas, for each of which hidden states are estimated. The body movement data with a small variance estimates three explicit states of the listener's speech, the speaker's speech, and silence, as well as two implicit states of the speaker's thinking and the speaker's laughter. On the other hand, the persona of large body movement variation indicates the same three explicit states, as well as the implicit state of the speaker's giving responses and an uninterpretable state. The result above indicates that it is possible to estimate almost all of the conversational states. Labeling manually some of the voice data, random forest models are constructed to know the importance of the variables. It turns out the mean of the body movements has the highest importance for the body movement with a small variance, while the maximum value of the voice per interval has the highest importance for that with a large variance.An initial prediction using only voice data presents the accuracy of 0.61 and 0.59 for body movement with small variance and for that with large variance, respectively. On the contrary, the prediction using body movement greatly improves the accuracy to 0.96 and 0.99 for body movement with small variance and large variance, respectively. This suggests that body movement is useful for estimating the conversational state.The method for state estimation enables us to automate the labeling of conversational states that previously had to be done manually.Furthermore, the method to find hidden conversational states the analyst has not assumed can provide a stepping stone to facilitating listening. The uninterpretable state may come up because of the shortage of information to be fed to the model.The need for biometric data other than body movements and video data during listening is indicated to interpret the uninterpretable state.
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Sofronov, Georgy. "A hybrid algorithm for spatial small area estimation under models with complex contiguity." In 2013 IEEE Symposium on Differential Evolution (SDE). IEEE, 2013. http://dx.doi.org/10.1109/sde.2013.6601438.
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Chen, Wei, Xiaokai Huang, Zijian Li, Ruichu Cai, Zhiyi Huang, and Zhifeng Hao. "Individual Causal Structure Learning from Population Data." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/786.
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Learning the causal structure of each individual plays a crucial role in neuroscience, biology, and so on. Existing methods consider data from each individual separately, which may yield inaccurate causal structure estimations in limited samples. To leverage more samples, we consider incorporating data from all individuals as population data. We observe that the variables of all individuals are influenced by the common environment variables they share. These shared environment variables can be modeled as latent variables and serve as a bridge connecting data from different individuals. In particular, we propose an Individual Linear Acyclic Model (ILAM) for each individual from population data, which models the individual's variables as being linearly influenced by their parents, in addition to environment variables and noise terms. Theoretical analysis shows that the model is identifiable when all environment variables are non-Gaussian, or even if some are Gaussian with an adequate diversity in the variance of noises for each individual. We then develop an individual causal structures learning method based on the Share Independence Component Analysis technique. Experimental results on synthetic and real-world data demonstrate the correctness of the method even when the sample size of each individual's data is small.
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Bass, D., and K. Haddara. "Roll Damping For Small Fishing Vessels." In SNAME 22nd American Towing Tank Conference. SNAME, 1989. http://dx.doi.org/10.5957/attc-1989-050.
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The successes of Naval Architects in predicting ship motions in waves, have been mainly confined to motions other than roll. For roll motions it has long been recognized that roll damping for wave encounter frequencies near the natural roll frequency is not only an extremely significant parameter but also very difficult to predict accurately. Because of the need to consider viscous flow (or even 'slightly viscous flow') to correctly model roll damping phenomena, there is still some way to go before an adequate numerical model of the hydrodynamics of roll is forthcoming. For this reason empirical and semi-empirical methods have played and will continue to play an important role in the prediction of roll motions. For small fishing vessels with deep skegs, hard chines and nonstandard hull shapes, the prediction of roll damping is particularly difficult due to lack of available data bases, semi- empirical formulae, and of course adequate theoretical models. It was for this reason that the present study was undertaken. In all, six small fishing vessels will be studied, each of which lies in the 'less-than 25 meter' class. They are all of similar dimensions but have varying hull forms ranging from the angular (e.g. model '363') to the rounded hull form of '366' (see figure 1). Apart from providing a data base for the estimation of damping for different hull forms, the study will be used in the analysis of mathematical and/or numerical methods for the prediction of roll damping that the authors hope to develop (or hope will be developed) in the future. The present paper describes preliminary investigations of roll damping characteristics for just 3 of the boats. The methodology employed was that of the classical roll decay test with an innovative feature-namely the use of a newly developed method of analysis which enabled the authors to obtain the non- dimensional damping coefficient from the complete roll decay curve taken over just one full cycle. This method of analysis is based on an energy approach and is explained in [l]. Using this approach, the roll damping moment dependence on the initial roll angle is easy to obtain. The emphasis in this paper is a 'frequency domain' analysis of the results with equivalent linear damping as the primary target. The advantage of the simple decay test is that it allows for analysis in both the frequency and time domains. A study of the results in the time domain will be presented in a later paper. The simplicity of the roll decay experiment also means that many experiments can be perfomed and regression analysis carried out on the results. Over one thousand such tests were performed for the three models in this study. The body plans for the three models are shown in figures 1 (a), (b), (c), and their particulars are given in Table l. In the experiments the models were attached to a dynamometer with just 2 degrees of freedom; the model was free to roll and heave, but restrained in all other modes.
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Drake, Arthur, Jun Wang, Qiuyi Chen, Ardalan Nejat, James Guest, and Mark Fuge. "To Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142052.
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Abstract In Topology Optimization (TO) and related engineering applications, physics-constrained simulations are often used to optimize candidate designs given some set of boundary conditions. However, such models are computationally expensive and do not guarantee convergence to a desired result, given the frequent non-convexity of the performance objective. Creating data-based approaches to warm-start these models — or even replace them entirely — has thus been a top priority for researchers in this area of engineering design. In this paper, we present a new dataset of two-dimensional heat sink designs optimized via Multiphysics Topology Optimization (MTO). Further, we propose an augmented Vector-Quantized GAN (VQGAN) that allows for effective MTO data compression within a discrete latent space, known as a codebook, while preserving high reconstruction quality. To concretely assess the benefits of the VQGAN quantization process, we conduct a latent analysis of its codebook as compared to the continuous latent space of a deep AutoEncoder (AE). We find that VQGAN can more effectively learn topological connections despite a high rate of data compression. Finally, we leverage the VQGAN codebook to train a small GPT-2 model, generating thermally performant heat sink designs within a fraction of the time taken by conventional optimization approaches. We show the transformer-based approach is more effective than using a Deep Convolutional GAN (DCGAN) due to its elimination of mode collapse issues, as well as better preservation of topological connections in MTO and similar applications.
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Diz-Rosales, Naomi, María José Lombardía, and Domingo Morales. "Mapping the Poverty Proportion in Small Areas under Random Regression Coefficient Poisson Models." In Congreso XoveTIC: impulsando el talento científico (6º. 2023. A Coruña). Servizo de Publicacions. Universidade da Coruña, 2023. http://dx.doi.org/10.17979/spudc.000024.18.
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In a complex socio-economic context, policy makers need highly disaggregated poverty indicators. In this work, we develop a methodology in small area estimation to derive predictors of poverty proportions under a random regression coefficient Poisson model, introducing bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and random effects mode predictors are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimator. The new statistical methodology is applied to data from the Spanish survey of living conditions to map poverty proportions by province and sex, developing a tool to support policy decision making
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Терехин, Э. А. "ASSESSMENT OF CHANGES IN THE FOREST OF CENTRAL RUSSIAN UPLAND USING REMOTE SENSING DATA." In Лесные экосистемы в условиях изменения климата: биологическая продуктивность и дистанционный мониторинг. Crossref, 2020. http://dx.doi.org/10.25686/7234.2020.6.58832.
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Оценка изменений в лесах, обусловленных влиянием негативных факторов, является одной из ключевых задач мониторинга лесных земель. В статье изложен новый подход к автоматизированному геоинформационному картографированию участков лесов с нарушенным древостоем. Метод основан на оценке изменений в высотах лесных насаждений с использованием спектрального отклика. Исследование выполнено на примере лесных массивов юга Среднерусской возвышенности, охватывающего территорию Белгородской области. Для оценки высот предложено использовать логистическую модель, описывающую зависимость коэффициентов спектральной яркости SWIRдиапазона от высоты древостоя. Для повышения точности оценки высот предложено использовать виды моделей, использующие спектральные характеристики с конкретных спутниковых сенсоров. Критерием индикации участков нарушенности древостоя является резкое снижение высоты лесных насаждений, обусловленное влиянием негативных факторов. Ограничениями применения метода являются региональные особенности лесных экосистем, наличие участков теней от лесных массивов, расположенных на крутых склонах. С использованием предложенного подхода выполнено геоинформационное картографирование изменений в лесах юга Среднерусской возвышенности в период середины 1980-х – конец 2010-х гг. Подготовлена серия картосхем, пространственно характеризующих произошедшие изменения. С использованием средств геоинформационного анализа выполнена оценка доли нарушенных лесных экосистем в каждом административном районе Белгородской области. В большинстве районов произошло снижение доли нарушенных лесных участков. Установлено, что в исследуемый период доля нарушенных лесных участков на юге Среднерусской возвышенности была сравнительно невелика и составила 1,84 % от общей площади лиственных лесов в середине 1980-х – конце 1990-х гг. и 0,85% в период 2000-2017 гг. The assessment of changes in forests caused by the influence of negative factors is one of the key objectives of forest monitoring. The article presents a new approach to automated mapping of forest areas with disturbed stands. The method is based on the estimation of changes in the heights of forest stands using a spectral response. The research was carried out using forest areas in the south of the Central Russian Upland, in the Belgorod Region. In order to assess the heights of the stand, it was proposed to use a logistic model that describes the dependence of the SWIR range spectral response on the height of the forest stand. In order to improve the accuracy of estimating stand heights, it was proposed to use the types of models that use spectral characteristics from specific satellite sensors. The criterion for identifying areas of disturbance in the stand is a significant decrease in the height of forest stands, occurring as a result of negative factors impact. The regional features of forests and shadowed areas on steep slopes can be considered to be the major limitations of the method. The proposed approach enabled the authors to carry out geoinformation mapping of the changes in the forests in the south of the Central Russian Upland over the period from the mid-1980s to the end of 2010s. Schematic maps characterizing the occurring changes have been prepared. Using geoinformation analysis, the share of disturbed forest ecosystems in each administrative district of the Belgorod Region has been estimated. In many districts, the share of disturbed forest ecosystems has decreased. During the reference period the share of disturbed forest areas in the south of the Central Russian Upland was relatively small. It was 1.84% of the total deciduous forest area in the mid-1980s - late 1990s and 0.85% in the period 2000-2017.
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Escalante, Alfredo, Manuel Sanjurjo Rivo, and Pablo Ghiglino. "Bennunet - Applying Machine Learning Techniques for Autonomous Optical Relative Navigation of an Asteroid." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-015.
Texte intégralRésumé :
Small Solar System bodies have been the target of space missions since the ICE (International Cometary Explorer) crossed the plasma tail of Comet Giacobini-Zinner on September 11, 1985, and became the first spacecraft visitor of a comet. The main challenge of navigating small bodies is that the ephemeris and physical properties of the target are typically not known with enough accuracy for orbit determination. In addition, low-gravity field and non-uniform target shape means that orbit and attitude estimation must be calculated with the spacecraft on-board computer. Among the sensors used for on-board pose estimation and relative navigation in small bodies missions, monocular vision cameras enable the estimation of the relative position of the spacecraft with lower hardware complexity, mass, size, and power requirements. It is true that monocular vision requires more complex solutions for solving the relative position as monocular cameras are not able to directly resolve the distance to the target. But as soon as this algorithmic complexity is overcome, monocular vision sensors are applicable for a full pose estimation solution in low-resources missions. Bennunet is a hybrid neural network-based method, devoted to on-board spacecraft relative position and attitude estimation in the vicinity of minor bodies like asteroids, comets or small moons, using monocular camera sensor. In the context of navigating such minor bodies, traditional heuristic methods for spacecraft position and attitude determination encounter limitations in robustness and precision in the presence of adverse illumination conditions. Moreover, their performance is limited due to the computational cost resulting from the evaluation of a large number of possible pose hypotheses. In comparison, Bennunet directly learns the nonlinear transformation from a 2-D grayscale image to the 6-D pose vector space. Bennunet is conformed by a set of sequential convolutional neural networks (CNNs) organised in two levels. The high-level multiclass-classification CNN is in charge of determining the sector of the discretized 3D space. Then, based on the sector estimation, the image is ingested by a low-level regression CNN, trained specifically for that sector, which estimates the pose of the camera. In addition, a high-level regression CNN was added before the high-level classification with the purpose of estimating vertical and horizontal shift of the target centroid in the image. This de-shifting pre-processing substantially boosted the performance of the classification CNN. The secondary contribution of this research is the development of SPyRender, a tool for the generation of large sets of synthetic images, suitable for the training and testing of the designed CNNs. SPyRender implements GPU-accelerated physically-based rendering, enabling the efficient generation of photorealistic images. SPyRender has been used with 3-D models of asteroid Bennu for producing multiple image sets covering the whole range of camera position, attitude, illumination conditions, and target albedo map variation, allowing to study the impact of different geometries and image effects in the network performance. The architecture of the neural networks conforming Bennunet was originally based on AlexNet [1] with the purpose of setting up the basis of the application of CNNs for the use case of autonomous optical relative navigation [2]. However in later development stages, other architectures have been implemented for Bennunet, substantially improving its performance. Namely, the usage of Time Distributed CNNs (TdCNNs) takes advantage of the dynamics of the spacecraft orbiting the target to ingest sequences of frames instead of a single image, substantially improving estimation performance. In addition, Automated Machine Learning (AutoML) [3] techniques have been introduced in the architecture design process aiming to semi-automate the Neural Architecture Search (NAS) and efficiently explore the search space for Model Selection and Hyperparameter Optimization. Compared to previous works, the training process and data augmentation in this contribution have been extended by using multiple input image resolutions in the NAS process. Moreover, random target model variations have been introduced for the generation of training and test image sets. Finally, the designed Neural Networks have been tested with real images from the Osiris Rex mission [4]. References 1.A. Krizhevsky et al.,“ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, 2012. 2.A. Escalante et al.,“Churinet – A deep learning approach to optical navigation for minor bodies,” IAC2021 Proceedings, 2021. 3.F. Hutter et al, ‘’Automated Machine Learning’’, 2019, doi:10.1007/978-3-030-05318-5. 4.Rizk, B., “OCAMS: The OSIRIS-REx Camera Suite”, Space Science Reviews, vol. 214, no. 1, 2018. doi:10.1007/s11214-017-0460-7.
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Morales-Valdez, Jesús, and Luis Alvarez-Icaza. "Building Stiffness Estimation by Wave Traveling Times." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6314.
Texte intégralRésumé :
A novel technique to estimate stiffness in buildings is presented. In contrast with most of the available work in the literature that resorts to diverse forms of modal analysis, this local technique is based on the propagation of a Ricker pulse through the structure and on measuring the wave arrival times at each story of the building, represented as a single layer in a multiple stratum model. These arrival times are later used to recuperate building stiffness at each story. Wave propagation is based on the Thomson-Haskell method, that allows to generalize the wave propagation method to multi-story buildings without significant changes to the original formulation. The number of calculated parameters is small in comparison with methods based on modal analysis. This technique provides and quick and easy methodology to assess building integrity and is an interesting alternative to verify results obtained by other identification methods. Simulation results for building with heterogeneous characteristics across the stories confirm the feasibility of the proposal.
Rapports d'organisations sur le sujet "Latent Models, Small Area Estimation"
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Ulloa, Alfie, and Rodrigo Wagner. Why don't All Exporters Benefit from Free Trade Agreements?: Estimating Utilization Costs. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0011503.
Texte intégralRésumé :
Free Trade Agreements (FTA) attract significant interest, but after these treaties are signed not all exporters use them. We provide a model of heterogeneous utilization, also developing a novel method to estimate treaty-utilization costs. We later apply the model to estimate the evolution utilization costs for the FTA between the US and a small open economy, Chile. Consistent with other studies, we find that utilization is indeed partial (on average 67% on the first year of the treaty, with 10 percentage points more at the third year). This made tariff revenues to the US 10% higher than expected with full utilization. Our simple structural model identifies costs by exploiting the indifference condition for the smallest firm that uses the treaty. Empirically we find that estimated costs were very heterogeneous across products. For almost half the products the cost was not binding for any exporter. However, when the FTA started, the 75-th percentile of utilization cost was around US$3,000, requiring shipments above $80,000 to justify using the treaty. These costs decreased by 60-80% in the following years, consistent with models of learning about treaty use. As remarked in our model, small exporters that do not use the trade agreement could even suffer when large firms have the option of using the treaty, since the latter increase exports and may push up factor prices for the industry.