1,721,105 research outputs found
Estimating regional income indicators under transformations and access to limited population auxiliary information
Full text linkSpatially disaggregated income indicators are typically estimated by using model-based methods that assume access to auxiliary information from population micro-data. In many countries like Germany and the UK population micro-data are not publicly available. In this work we propose small area methodology when only aggregate population-level auxiliary information is available. We use data-driven transformations of the response to satisfy the parametric assumptions of the used models. In the absence of population micro-data, appropriate bias-corrections for small area prediction are needed. Under the approach we propose in this paper, aggregate statistics (means and covariances) and kernel density estimation are used to resolve the issue of not having access to population micro-data. We further explore the estimation of the mean squared error using the parametric bootstrap. Extensive model-based and design-based simulations are used to compare the proposed method to alternative methods. Finally, the proposed methodology is applied to the 2011 Socio-Economic Panel and aggregate census information from the same year to estimate the average income for 96 regional planning regions in German
Construction of regional consumer price indices using small area estimation
No full textConsumer Price Indices (CPI) are used in many ways by the government, businesses, and society in general. They can affect interest rates, tax allowances, wages, state benefits, and many other payments. The CPI is a fixed (national) basket index, where a range of goods and services is priced each month, and the expenditure shares on items in the basket are used to weight the price information together. The starting point for a regional price index should be a regional basket of goods and services. In the current poster, we derive regional baskets from the UK Living Costs and Food Survey (LCF), taking the products (COICOP classification) with the largest proportion of expenditures. As the sample size is naturally much smaller for regions, the accuracy of the direct estimates on the basket will be reduced. In order to overcome this problem one possibility - discussed in the poster - is to pool multiple years of LCF data to increase the sample size. Another is to consider small area estimation approaches for the regional basket. Ideally, the small area estimates would be constrained to the overall expenditure total. Therefore, we assess some benchmarking approaches. Since the conceptual framework of CPI-calculationfor the UK and Germany do not differ too much the presented methodology can also be adapted for the calculation of regional CPIs for Germany.<br/
Domain prediction with grouped income data
Full text linkOne popular small area estimation method for estimating poverty and inequality indicators is the empirical best predictor under the unit-level nested error regression model with a continuous dependent variable. However, parameter estimation is more challenging when the response variable is grouped due to data confidentiality concerns or concerns about survey response burden. The work in this paper proposes methodology that enables fitting a nested error regression model when the dependent variable is grouped. Model parameters are then used for small area prediction of finite population parameters of interest. Model fitting in the case of a grouped response variable is based on the use of a stochastic expectation–maximization algorithm. Since the stochastic expectation–maximization algorithm relies on the Gaussian assumptions of the unit-level error terms, adaptive transformations are incorporated for handling departures from normality. The estimation of the mean squared error of the small area parameters is facilitated by a parametric bootstrap that captures the additional uncertainty due to the grouping mechanism and the possible use of adaptive transformations. The empirical properties of the proposed methodology are assessed by using model-based simulations and its relevance is illustrated by estimating deprivation indicators for municipalities in the Mexican state of Chiapas
Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive geo-referenced administrative data protected via measurement error
No full textModern systems of official statistics require the timely estimation of area-specific densities of subpopulations. Ideally estimates should be based on precise geocoded information, which is not available because of confidentiality constraints. One approach for ensuring confidentiality is by rounding the geoco-ordinates. We propose multivariate non-parametric kernel density estimation that reverses the rounding process by using a measurement error model. The methodology is applied to the Berlin register of residents for deriving density estimates of ethnic minorities and aged people. Estimates are used for identifying areas with a need for new advisory centres for migrants and infrastructure for older people
A unit-level quantile nested error regression model for domain prediction with continuous and discrete outcomes
Full text linkIn this paper we will present recent work on a new unit-level small area methodology that can be used with continuous and discrete outcomes. The proposed method is based on constructing a model-based estimator of the distribution function by using a nested-error regression model for the quantiles of the target outcome. A general set of domain-specific parameters that extends beyond averages is then estimated by sampling from the estimated distribution function. For fitting the model we exploit the link between the Asymmetric Laplace Distribution and maximum likelihood estimation for quantile regression. The specification of the distribution of the random effects is considered in some detail by exploring the use of parametric and non-parametric alternatives. The use of the proposed methodology with discrete (count) outcomes requires appropriate transformations, in particular jittering. For the case of discrete outcomes the methodology relaxes the restrictive assumptions of the Poisson generalised linear mixed model and allows for what is potentially a more flexible mean-variance relationship. Mean Squared Error estimation is discussed. Extensive model-based simulations are used for comparing the proposed methodology to alternative unit-level methodologies for estimating a broad range of complex parameters
Random forests and mixed effects random forests for small area estimation of general parameters: a poverty mapping case study in Mozambique
No full textRobust Small Area Estimation Under Spatial Non-stationarity
Full text linkThe effective use of spatial information in a regression-based approach to small area estimation is an important practical issue. One approach to account for geographic information is by extending the linear mixed model to allow for spatially correlated random area effects. An alternative is to
include the spatial information by a non-parametric mixed models. Another option is geographic weighted regression where the model coefficients vary spatially across the geography of interest. Although these approaches are useful for estimating small area means efficiently under strict parametric assumptions, they can be sensitive to outliers. In this paper, we propose robust extensions of the geographically weighted empirical best linear unbiased predictor. In particular, we introduce robust projective and predictive estimators under spatial non-stationarity. Mean squared error estimation is performed by two analytic approaches that account for the spatial structure in the data. Model-based simulations show that the methodology proposed often leads to more efficient estimators. Furthermore, the analytic mean squared error estimators introduced have appealing
properties in terms of stability and bias. Finally, we demonstrate in the application that the new methodology is a good choice for producing estimates for average rent prices of apartments in urban planning areas in Berlin
Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal
Full text linkModern systems of official statistics require the accurate and timely estimation of sociodemographic indicators for disaggregated geographical regions. Traditional data collection methods such as censuses or household surveys impose great financial and organizational bur- dens on national statistical institutes. The rise of new information and communication technolo- gies offers promising sources to mitigate these shortcomings.We propose a unified approach for national statistical institutes in developing countries based on small area estimation that allows for the estimation of sociodemographic indicators by using mobile phone data. In particular, the methodology is applied to mobile phone data from Senegal for deriving subnational estimates of the share of illiterates disaggregated by gender. The estimates are used to identify hotspots of illiterates with a need for additional infrastructure or policy adjustments. Although we focus on literacy as a particular sociodemographic indicator, the approach proposed is applicable to indicators from national statistics in general
Smoothing and benchmarking for small area estimation
No full textSmall area estimation is concerned with methodology for estimating population parameters associated with a geographic area defined by a cross-classification that may also include non-geographic dimensions. In this paper, we develop constrained estimation methods for small area problems: those requiring smoothness with respect to similarity across areas, such as geographic proximity or clustering by covariates, and benchmarking constraints, requiring weighted means of estimates to agree across levels of aggregation. We develop methods for constrained estimation decision theoretically and discuss their geometric interpretation. The constrained estimators are the solutions to tractable optimisation problems and have closed-form solutions. Mean squared errors of the constrained estimators are calculated via bootstrapping. Our approach assumes the Bayes estimator exists and is applicable to any proposed model. In addition, we give special cases of our techniques under certain distributional assumptions. We illustrate the proposed methodology using web-scraped data on Berlin rents aggregated over areas to ensure privacy.</p
Conservative treatment of asymmetric ankle osteoarthritis
No full textThis review article summarizes the currently available (poor) evidence of conservative treatment of asymmetric ankle osteoarthritis in the literature and adds the authors' experience with the particular technique. The use of dietary supplementation, viscosupplementation, platelet-rich plasma, nonsteroidal anti-inflammotory drugs, corticosteroid injections, physical therapy, shoe modifications and orthoses, and patient's education in asymmetric ankle osteoarthritis is outlined. There definitively is a place for conservative treatment with reasonable success in patients whose ankles do not qualify anymore for joint-preserving surgery and in patients with medical or orthopedic contraindications for realignment surgery, total ankle replacement, and ankle arthrodesis
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