Small area estimation is important in light of a continual demand by data users for finer geographic detail of published statistics and for various subpopulations. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary. The socalled small area estimation sae methods 1,2 link survey data that is only available for a proportion of households with administrative or census data available. Comparing direct survey and small area estimates of health. Introduction small area estimation sae is a methodology for producing estimates for a more detailed level of geography than estimates using direct survey estimation. Project home search the entire project projects people documents advanced search. Small area disease risk estimation and visualization using r. Mcroberts, rasmus astrup norwegian institute of bioeconomy research climate center and national forest inventory p. Solutions and perspectives was prepared within the framework of the global strategy to improve agricultural and rural statistics. Small area estimation using spatiotemporal fayherriot model the methodology of spatiotemporal small area model was described in marhuenda, molina, and morales 20. It uses empirical best linear unbiased prediction eblup estimator. Time series models for small area estimation based on area level models.
Models provided range from the basic fayherriot model to its improvement such as youchapman. Small area analysis saa describes statistical methods or techniques used to produce adequate estimates when there is insufficient information or small sample size. Using r for small area estimation in the norwegian national forest inventory johannes breidenbach ronald e. Therefore there is no guarantee that any method for estimation in small unplanned domains will yield desirable precision in any domain. Small area estimation with a lognormal mixed model under informative sampling. From feasibility to implementation, by soon song 3 1. Package sae march 1, 2020 type package title small area estimation version 1.
Small area estimation of labour force indicators under a multinomial model with correlated time and area effects. At the moment, there is little training in this subject using r. License gpl2 needscompilation no repository cran datepublication 20200301 11. An r package for small area estimation by isabel molina and yolanda marhuenda abstract we describe the r package sae for small area estimation. Journal of the royal statistical society, series a. Functions to compute small area estimates based on a basic area or unitlevel model. This function compute the eblup estimator for small areas rao, 2003 using a area level model see references below.
In the latter case numerical integration is used to average over the posterior density for the between area variance. Improving estimates of district hiv prevalence and burden in. Spatial disaggregation and smallarea estimation methods for. This package can be used to obtain modelbased estimates for small areas based on a variety of models at the area and unit levels, along with basic direct and indirect estimates. Pdf producing small area estimation using r in the romanian. For high spatial correlation between domains, noticeable mse reduction was observed. In its essence the small area estimation presented here relies on using household survey data to estimate. Small area estimation of income using spatiotemporal models. Models with random or mixed effects are commonly used for panel data, in microarrays, small area estimation and many other applications. Multivariate fay herriot models for small area estimation. Small area estimation deals with the problem of providing estimates of some parameters of interest for subpopulations.
This paper presents a brief outline of the theory underlying each package, as well as an. The model is fit using restricted maximum likelihood, or in a hierarchical bayesian way. In this paper, we demonstrate how r can be used to obtain disease risk estimates and quantify risk factors using areal data. You can find more information and examples in the vignette included in the package. These techniques are often required when data from a larger survey are available but parameter estimates are required at a smaller level. Traditional demographic sample surveys designed for national estimates do not provide large enough samples to produce reliable direct estimates for. However, precision is ultimately limited by survey sample size.
Mean squared errors are estimated by analytical approximations in simple models and applying bootstrap procedures in more complex models. Using smallarea estimation to calculate the prevalence of. Business data are frequently characterised by skewed distributions, with a few large enterprises that account for the majority of the total for the variable of interest, for example turnover. Small area estimation in r with application to mexican. Hence, modelbased small area estimation techniques that increase the effective sample size by borrowing strength are needed. While the option of more specific surveys is costly, modelbased methodologies for dealing with small sample sizes can help to obtain reliable estimates for small domains.
A stata package for unit level small area estimation english abstract. A stata package for unit level small area estimation. Sometimes, we use small area estimation with small domain estimation. Generalized linear models for small area estimation malay ghosh, kannan natarajan, t. A range of indicators that includes the mean of the target variable, the quantiles of its distribution and. The r package emdi for estimating and mapping regionally. Rao carleton university, ottawa, canada paper presented at the ntts 20 conference, brussels. However, the normal distribution is not always well suited for modeling data on certain variables, such as those found in.
The r package sae is mainly designed for modelbased small area estimation. An r package for small area estimation sae 2014, poznan 040920 6 34. Basic small area estimation functions, such as the eblup, have been included in the sample software application functions are developed for the r statistical software m. Either maximum likelihood ml or restricted reml can be used in the estimation. Pdf producing small area estimation using r in the. We generated countylevel estimates of the proportion of adults with health insurance coverage insured rate using the new york brfss data. The small area estimation sae methods included in the r package sae have applications in many different. Also based on unit and area level models are the functions in the package hbsae 7. The mean of the target variable, the quantiles of its distribution, the head. R package returning estimates and estimates of mspe of variance components in the fayherriot model. Density estimation in r henry deng and hadley wickham september 2011 abstract density estimation is an important statistical tool, and within r there are over 20 packages that implement it. Eblup estimator for small areas in becarioprecario. This paper presents a new family of stata functions devoted to small area estimation.
The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. One way to analyze this information is by utilizing small area analysis. The example data comes from mukhopadhyay and mcdowell, small area estimation for survey data analysis using sas software. Small area estimation can improve estimates of hiv prevalence in small, unplanned analysis domains. The r package emdi for the estimation and mapping of regional. This technical report on spatial disaggregation and smallarea estimation methods for agricultural surveys. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areassubpopulations. The workshop will start with an introduction to the analysis of survey data with designbased estimators, using packages survey and sampling. When the sample size is not enough to provide reliable estimates at a very particular level, the power of models and auxiliary information must be applied with no hesitation. The goal was to demonstrate the small area estimation sae technique using the readilyavailable r package bayessae. An r package for gaussian, poisson, and binomial random e ects models. The purpose of this paper is to reveal the opportunities found in the romanian official statistics to develop a longway but strong implementation of the small area estimation techniques, together.
The package may allow the prospective researcher during the research. Small area estimation using esds government surveys. If an auxiliary variable exists that is correlated with the variable of interest, small area estimation sae provides methods to solve the problem rao 2003, rao and molina 2015. A generalized mixed model for skewed distributions applied to. Small area estimation sae has become a widely used technique in official statistics since the last decade of past century. The r package emdi o ers a methodological and computational framework for the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for assessing, processing and presenting the results. Carlin bayesian methods have been used quite extensively in recent years for solving small area estimation problems. Multilevel regression and poststratification for smallarea estimation of population health outcomes.
The study was carried out for incomerelated variables drawn from the polish household budget survey and explanatory variables from the polish local data bank for the years 200320. An r package for small area estimation with multinomial. Introduction to small area estimation small area estimation in r application to mexican income data conclusion small area estimation in r with application to mexican income data annkristin kreutzmann1, s oren pannier1 natalia rojasperilla1, timo schmid1 matthias templ2 nikos tzavidis3 1 freie universit at berlin. The r package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. When the sample size is not enough to provide reliable estimates at a very particular level, the power of models and auxiliary. Introduction to small area estimation small area estimation in r application to mexican income data conclusion r packages for sae other packages i bayessae for area level models in bayesian context i saerobust for robust area level models i saery and sae2 for area level models with time e ects i sae for a wide variety of sae methods including area level and unitlevel models for the mean as. The purpose of this package is primarily to document the functions used in the publications breidenbach and astrup 2012 and breidenbach et al. May 05, 2016 effect of small area estimation on health reporting area rates among the 48 hras in king county, smoking prevalence rates from direct estimation ranged from 5. For a comprehensive account of sae techniques, seerao2003. Jul 14, 2016 zhang x, holt jb, lu h, wheaton ag, ford es, greenlund kj, et al. A typical income consumption or living standards survey collects detailed information that can be used for estimation of our statistic of interest. Abstract we describe the r package sae for small area estimation.
The seminar application of small area estimation techniques in the social sciences begins to keynote speech by professor jnk rao, of carlenton university at canada. It also contains data and a vignette that explain its. Small area estimation sae is an umbrella term to describe a number of approaches that provide reliable small area statistics even when only very small samples are available for these areas. The introduced r package saesim is adjusted to provide a simulation environment for the special case of small area estimation. Description this package currently implements the unit level eblup battese et al. R forge provides these binaries only for the most recent version of r, but not for older versions. Small area estimation i small area estimation provides a general framework for investigating the spatial distribution of variables at di. Apr 16, 2017 small area estimation sae has become a widely used technique in official statistics since the last decade of past century. If you get the code to run, id appreciate hearing that it still works. Below is a list of all packages provided by project robust small area estimation important note for package binaries.
Morales universities of somewhere and elsewheremme. Small area estimation in r with application to mexican income. The 90% confidence interval ci halfwidth ranged from 2. Small area estimation techniques combine multiple data sources to capitalize on each data sources strengths. This package provides a variety of functions to deal with several specific small area area level models in bayesian context. A global analysis strategy to resolve neutrino nsi degeneracies with scattering and oscillation. A methodological approach to small area estimation for the. Chapter 21 small area estimation data science with r. It appears you dont have a pdf plugin for this browser.
Small area estimation with a lognormal mixed model under. Introduction to small area estimation sae package developers 22nd january 2007 1 introduction in this vignette we will describe an example on how to produce small area estimates using di. The r package emdi for estimating and mapping regionally disaggregated indicators. Small area disease risk estimation and visualization using r by paula moraga abstract small area disease risk estimation is essential for disease prevention and control. Statistics in transition new series estimation of income. When the variable of interest is continuous, normality is commonly assumed, either in the original scale or after some transformation. An objectoriented framework for statistical simulation. You can click here to download the reference manual.
377 1396 817 1316 811 1148 987 751 1297 21 933 384 53 680 1088 557 1306 1330 1486 172 1491 209 1156 94 627 93 602 1215 194 841 1401 1186 194 95 57 1048 1270 1314