235,151 research outputs found
mplot: An R Package for Graphical Model Stability and Variable Selection Procedures
The mplot package provides an easy to use implementation of model stability
and variable inclusion plots (M\"uller and Welsh 2010; Murray, Heritier, and
M\"uller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008;
Jiang, Nguyen, and Rao 2009) for linear and generalised linear models. We
provide a number of innovations on the standard procedures and address many
practical implementation issues including the addition of redundant variables,
interactive visualisations and approximating logistic models with linear
models. An option is provided that combines our bootstrap approach with glmnet
for higher dimensional models. The plots and graphical user interface leverage
state of the art web technologies to facilitate interaction with the results.
The speed of implementation comes from the leaps package and cross-platform
multicore support.Comment: 28 pages, 9 figure
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
Reference values: a review
Reference values are used to describe the dispersion of variables in healthy individuals. They are usually reported as population-based reference intervals (RIs) comprising 95% of the healthy population. International recommendations
state the preferred method as a priori nonparametric determination from at least 120 reference individuals, but acceptable alternative methods include transference or validation from previously established RIs. The most critical steps in the determination of reference values are the selection of reference individuals based on extensively documented inclusion and exclusion criteria and the use of quality-controlled analytical procedures. When only small numbers of values are available, RIs can be estimated by
new methods, but reference limits thus obtained may be highly imprecise. These recommendations are a challenge in veterinary clinical pathology, especially when only small numbers of reference individuals are available
Inference for High-Dimensional Sparse Econometric Models
This article is about estimation and inference methods for high dimensional
sparse (HDS) regression models in econometrics. High dimensional sparse models
arise in situations where many regressors (or series terms) are available and
the regression function is well-approximated by a parsimonious, yet unknown set
of regressors. The latter condition makes it possible to estimate the entire
regression function effectively by searching for approximately the right set of
regressors. We discuss methods for identifying this set of regressors and
estimating their coefficients based on -penalization and describe key
theoretical results. In order to capture realistic practical situations, we
expressly allow for imperfect selection of regressors and study the impact of
this imperfect selection on estimation and inference results. We focus the main
part of the article on the use of HDS models and methods in the instrumental
variables model and the partially linear model. We present a set of novel
inference results for these models and illustrate their use with applications
to returns to schooling and growth regression
Prior distributions for objective Bayesian analysis
We provide a review of prior distributions for objective Bayesian analysis. We start by examining some foundational issues and then organize our exposition into priors for: i) estimation or prediction; ii) model selection; iii) highdimensional models. With regard to i), we present some basic notions, and then move to more recent contributions on discrete parameter space, hierarchical models, nonparametric models, and penalizing complexity priors. Point ii) is the focus of this paper: it discusses principles for objective Bayesian model comparison, and singles out some major concepts for building priors, which are subsequently illustrated in some detail for the classic problem of variable selection in normal linear models. We also present some recent contributions in the area of objective priors on model space.With regard to point iii) we only provide a short summary of some default priors for high-dimensional models, a rapidly growing area of research
Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing
We consider the problems of hypothesis testing and model comparison under a
flexible Bayesian linear regression model whose formulation is closely
connected with the linear mixed effect model and the parametric models for SNP
set analysis in genetic association studies. We derive a class of analytic
approximate Bayes factors and illustrate their connections with a variety of
frequentist test statistics, including the Wald statistic and the variance
component score statistic. Taking advantage of Bayesian model averaging and
hierarchical modeling, we demonstrate some distinct advantages and
flexibilities in the approaches utilizing the derived Bayes factors in the
context of genetic association studies. We demonstrate our proposed methods
using real or simulated numerical examples in applications of single SNP
association testing, multi-locus fine-mapping and SNP set association testing
Reformulating empirical macro-econometric modelling
The policy implications of estimated macro-econometric systems depend on the formulations of their equations, the methodology of empirical model selection and evaluation, the techniques of policy analysis, and their forecast performance. Drawing on recent results in the theory of forecasting, we question the role of `rational expectations'; criticize a common approach to testing economic theories; show that impulse-response methods of evaluating policy are seriously flawed; and question the mechanistic derivation of forecasts from econometric systems. In their place, we propose that expectations should be treated as instrumental to agents' decisions; discuss a powerful new approach to the empirical modelling of econometric relationships; offer viable alternatives to studying policy implications; and note modifications to forecasting devices that can enhance their robustness to unanticipated structural breaks. Keywords; economic policy analysis, macro-econometric systems, empirical model selection and evaluation, forecasting, rational expectations, impulse-response analysis, structural breaks
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