Results 61 to 70 of about 21,776,804 (321)
An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout
Journal of Probability and Statistics, 2019 In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared ...
Amal Almohisen +2 more
doaj +1 more source
Generalized Information Matrix Tests for Detecting Model Misspecification
Econometrics, 2016 Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies.
Richard M. Golden +3 more
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Agribusiness, EarlyView.ABSTRACT The origin of a product, if associated with good quality, can contribute to building a positive collective reputation, leading to a potential price premium. However, it is conceivable that a producer markets a product by evoking symbols, images, words, and values typical of places other than where it was designed or produced, creating a ...
Annalisa Caloffi +2 more
wiley +1 more source
BMC Medical Research MethodologyBackground Causal mediation analysis is widespread in applied medical research, especially in longitudinal settings. However, estimating natural mediational effects in such contexts is often difficult because of the presence of post-treatment confounding.
Chiara Di Maria, Vanessa Didelez
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Regression Based Expected Shortfall Backtesting
, 2019 This paper introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for the ES stand-alone is infeasible, and thus, our tests are based on a joint
Bayer, Sebastian, Dimitriadis, Timo
core +1 more source
Social Science Research Network, 2019 Standard tests and confidence sets in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty.
D. Andrews, S. Kwon
semanticscholar +1 more source
Strategic Misspecification in Regression Models [PDF]
American Journal of Political Science, 2003 Common regression models are often structurally inconsistent with strategic interaction. We demonstrate that this “strategic misspecification” is really an issue of structural (or functional form) misspecification. The misspecification can be equivalently written as a form of omitted variable bias, where the omitted variables are nonlinear terms ...
Curtis S. Signorino, Kuzey Yilmaz
openaire +1 more source
Market Selection and Learning Under Model Misspecification
Journal of Economic Dynamics and Control, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Giulio Bottazzi +2 more
openaire +3 more sources
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Welfare Cost of Model Uncertainty in a Small Open Economy
Entropy, 2020 This paper extends the canonical small open-economy real-business-cycle model, when considering model uncertainty. Domestic households have multiplier preferences, which leads them to take robust decisions in response to possible model misspecification ...
Jocelyn Tapia Stefanoni
doaj +1 more source