14,731 research outputs found
Two Procedures for Robust Monitoring of Probability Distributions of Economic Data Streams induced by Depth Functions
Data streams (streaming data) consist of transiently observed, evolving in
time, multidimensional data sequences that challenge our computational and/or
inferential capabilities. In this paper we propose user friendly approaches for
robust monitoring of selected properties of unconditional and conditional
distribution of the stream basing on depth functions. Our proposals are robust
to a small fraction of outliers and/or inliers but sensitive to a regime change
of the stream at the same time. Their implementations are available in our free
R package DepthProc.Comment: Operations Research and Decisions, vol. 25, No. 1, 201
KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory
Item response theory (IRT) models are a class of statistical models used to
describe the response behaviors of individuals to a set of items having a
certain number of options. They are adopted by researchers in social science,
particularly in the analysis of performance or attitudinal data, in psychology,
education, medicine, marketing and other fields where the aim is to measure
latent constructs. Most IRT analyses use parametric models that rely on
assumptions that often are not satisfied. In such cases, a nonparametric
approach might be preferable; nevertheless, there are not many software
applications allowing to use that. To address this gap, this paper presents the
R package KernSmoothIRT. It implements kernel smoothing for the estimation of
option characteristic curves, and adds several plotting and analytical tools to
evaluate the whole test/questionnaire, the items, and the subjects. In order to
show the package's capabilities, two real datasets are used, one employing
multiple-choice responses, and the other scaled responses
Count data models with variance of unknown form: an application to a hedonic model of worker absenteeism
We examine an econometric model of counts of worker absences due to illness in a sluggishly adjusting hedonic labor market. We compare three estimators that parameterize the conditional variance?least squares, Poisson, and negative binomial pseudo maximum likelihood?to generalized least squares (GLS) using nonparametric estimates of the conditional variance. Our data support the hedonic absenteeism model. Semiparametric GLS coefficients are similar in sign, magnitude, and statistical significance to coefficients where the mean and variance of the errors are specified ex ante. In our data, coefficient estimates are sensitive to a regressor list but not to the econometric technique, including correcting for possible heteroskedasticity of unknown form.Publicad
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