708 research outputs found
Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods
Linear mixed models underpin many small area estimation (SAE) methods. In this paper we investigate SAE based on linear models with spatially correlated small area effects where the neighbourhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in SAE. In particular, we use simulation studies to compare the performances of model-based direct estimation (MBDE) and empirical best linear unbiased prediction (EBLUP) under such models. These simulations are based on theoretically generated populations as well as data obtained from two real populations (the ISTAT farm structure survey in Tuscany and the US Environmental Monitoring and Assessment Program survey). Our empirical results show only marginal gains when spatial dependence between areas is incorporated into the SAE model
Disease Mapping via Negative Binomial Regression M-quantiles
We introduce a semi-parametric approach to ecological regression for disease
mapping, based on modelling the regression M-quantiles of a Negative Binomial
variable. The proposed method is robust to outliers in the model covariates,
including those due to measurement error, and can account for both spatial
heterogeneity and spatial clustering. A simulation experiment based on the
well-known Scottish lip cancer data set is used to compare the M-quantile
modelling approach and a random effects modelling approach for disease mapping.
This suggests that the M-quantile approach leads to predicted relative risks
with smaller root mean square error than standard disease mapping methods. The
paper concludes with an illustrative application of the M-quantile approach,
mapping low birth weight incidence data for English Local Authority Districts
for the years 2005-2010.Comment: 23 pages, 7 figure
Bootstrap for estimating the mean squared error of the spatial EBLUP
This work assumes that the small area quantities of interest follow a Fay-Herriot model with
spatially correlated random area effects. Under this model, parametric and nonparametric
bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical
Best Linear Unbiased Predictor). A simulation study compares the bootstrap estimates with an
asymptotic analytical approximation and studies the robustness to non-normality. Finally, two
applications with real data are described
Bootstrap for estimating the mean squared error of the spatial EBLUP
This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical Best Linear Unbiased Predictor). A simulation study compares the bootstrap estimates with an asymptotic analytical approximation and studies the robustness to non-normality. Finally, two applications with real data are described.
Parametric modeling of quantile regression coefficient functions with count data
AbstractApplying quantile regression to count data presents logical and practical complications which are usually solved by artificially smoothing the discrete response variable through jittering. In this paper, we present an alternative approach in which the quantile regression coefficients are modeled by means of (flexible) parametric functions. The proposed method avoids jittering and presents numerous advantages over standard quantile regression in terms of computation, smoothness, efficiency, and ease of interpretation. Estimation is carried out by minimizing a "simultaneous" version of the loss function of ordinary quantile regression. Simulation results show that the described estimators are similar to those obtained with jittering, but are often preferable in terms of bias and efficiency. To exemplify our approach and provide guidelines for model building, we analyze data from the US National Medical Expenditure Survey. All the necessary software is implemented in the existing R package
Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators
The Italian National Institute for Statistics regularly provides estimates of
unemployment indicators using data from the Labor Force Survey. However, direct
estimates of unemployment incidence cannot be released for Local Labor Market
Areas. These are unplanned domains defined as clusters of municipalities; many
are out-of-sample areas and the majority is characterized by a small sample
size, which render direct estimates inadequate. The Empirical Best Predictor
represents an appropriate, model-based, alternative. However, for non-Gaussian
responses, its computation and the computation of the analytic approximation to
its Mean Squared Error require the solution of (possibly) multiple integrals
that, generally, have not a closed form. To solve the issue, Monte Carlo
methods and parametric bootstrap are common choices, even though the
computational burden is a non trivial task. In this paper, we propose a
Semi-Parametric Empirical Best Predictor for a (possibly) non-linear mixed
effect model by leaving the distribution of the area-specific random effects
unspecified and estimating it from the observed data. This approach is known to
lead to a discrete mixing distribution which helps avoid unverifiable
parametric assumptions and heavy integral approximations. We also derive a
second-order, bias-corrected, analytic approximation to the corresponding Mean
Squared Error. Finite sample properties of the proposed approach are tested via
a large scale simulation study. Furthermore, the proposal is applied to
unit-level data from the 2012 Italian Labor Force Survey to estimate
unemployment incidence for 611 Local Labor Market Areas using auxiliary
information from administrative registers and the 2011 Census
Modelling the distribution of health related quality of life of advancedmelanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression
Health-related quality of life assessment is important in the clinical
evaluation of patients with metastatic disease that may offer useful
information in understanding the clinical effectiveness of a treatment. To
assess if a set of explicative variables impacts on the health-related quality
of life, regression models are routinely adopted. However, the interest of
researchers may be focussed on modelling other parts (e.g. quantiles) of this
conditional distribution. In this paper, we present an approach based on
quantile and M-quantile regression to achieve this goal. We applied the
methodologies to a prospective, randomized, multi-centre clinical trial. In
order to take into account the hierarchical nature of the data we extended the
M-quantile regression model to a three-level random effects specification and
estimated it by maximum likelihood
Robust small area estimation under spatial non-stationarity
Geographically weighted small area methods have been studied in literature for
small area estimation. Although these approaches are useful for the estimation
of small area means efficiently under strict parametric assumptions, they can
be very sensitive to outliers in the data. In this paper, we propose a robust
extension of the geographically weighted empirical best linear unbiased
predictor (GWEBLUP). In particular, we introduce robust projective and
predictive small area estimators under spatial non-stationarity. Mean squared
error estimation is performed by two different analytic approaches that
account for the spatial structure in the data. The results from the model-
based simulations indicate that the proposed approach may lead to gains in
terms of efficiency. Finally, the methodology is demonstrated in an
illustrative application for estimating the average total cash costs for farms
in Australia
The Challenge of Measuring Corporate Social Irresponsibility
In this paper, we develop a family of indexes to measure the social irresponsibility
of firms. We define corporate social irresponsibility (CSIR) on the basis
of firms’ alleged involvement in human rights abuses. After a critical appraisal
of the existing CSIR raw data and measures/indexes, we take a M-quantile
regression approach to develop a family of CSIR indexes that overcome the
limitations of existing measures. We apply our methodology to a sample of
380 large publicly-listed firms, observed over the period 2004-2012. Our analysis
develops a family of CSIR indexes robust to firms’ media exposure, size
and industry specificities, and provides a measure of their accurac
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