Results 81 to 90 of about 21,776,804 (321)
Econometrics, 2019 This paper provides an overview of a time-varying Structural Panel Bayesian Vector Autoregression model that deals with model misspecification and unobserved heterogeneity problems in applied macroeconomic analyses when studying time-varying ...
Antonio Pacifico
semanticscholar +1 more source
Rank‐based estimation of propensity score weights via subclassification
Canadian Journal of Statistics, EarlyView.Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang +3 more
wiley +1 more source
A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function
Population Health Metrics, 2012 Background The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses.
Rand-Hendriksen Kim +2 more
doaj +1 more source
Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification
Entropy, 2020 In this paper, we consider prediction and variable selection in the misspecified binary classification models under the high-dimensional scenario. We focus on two approaches to classification, which are computationally efficient, but lead to model ...
Konrad Furmańczyk, Wojciech Rejchel
doaj +1 more source
On model selection and model misspecification in causal inference [PDF]
Statistical Methods in Medical Research, 2010 Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators.
Vansteelandt, Stijn +2 more
openaire +3 more sources
T‐calibration in semi‐parametric models
Canadian Journal of Statistics, EarlyView.Abstract This article relates the calibration of models to the consistent loss functions for the target functional of the model. Correctly specified models are calibrated. Conversely, we demonstrate that if there is a parameter value that is optimal under all consistent loss functions, then a model is calibrated.
Anja Mühlemann, Johanna Ziegel
wiley +1 more source
Can quartet analyses combining maximum likelihood estimation and Hennigian logic overcome long branch attraction in phylogenomic sequence data? [PDF]
PLoS ONE, 2017 Systematic biases such as long branch attraction can mislead commonly relied upon model-based (i.e. maximum likelihood and Bayesian) phylogenetic methods when, as is usually the case with empirical data, there is model misspecification.
Patrick Kück +4 more
doaj +1 more source
Robust control and model misspecification [PDF]
Journal of Economic Theory, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hansen, Lars Peter +3 more
openaire +1 more source
To What Extent Does ESG Performance Influence Board Engagement in Acquisition Activity?
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This study examines the relationship between boards and corporate acquisition activity. Specifically, we posit that boards with directors who have been politicians positively influence the propensity to pursue acquisitions and that ESG performance (divided into environmental, social, and governance scores) moderates this relationship.
Leticia Pérez‐Calero +4 more
wiley +1 more source
Empirical likelihood estimation of the spatial quantile regression [PDF]
, 2012 The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model.
A Owen +32 more
core +1 more source