Results 21 to 30 of about 79,637 (281)

Likelihood-based estimation and prediction for a measles outbreak in Samoa

Infectious Disease Modelling, 2023
Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these
David Wu, Helen Petousis-Harris, Janine Paynter, Vinod Suresh, Oliver J. Maclaren   +4 more
doaj   +1 more source

MTE with Misspecification

, 2022
This paper studies the implication of a fraction of the population not responding to the instrument when selecting into treatment. We show that, in general, the presence of non-responders biases the Marginal Treatment Effect (MTE) curve and many of its functionals. Yet, we show that, when the propensity score is fully supported on the unit interval, it
Martínez-Iriarte, Julián, Spini, Pietro Emilio   +1 more
openaire   +2 more sources

Characterization of the asymptotic distribution of semiparametric M-estimators [PDF]

, 2010
This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification.
Ichimura, H, Lee, S
core   +3 more sources

Minimizing sensitivity to model misspecification [PDF]

Quantitative Economics, 2018
We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments.
Weidner, Martin, Bonhomme, Stéphane
openaire   +7 more sources

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, Steven S. Henley, Halbert White, T. Michael Kashner   +3 more
doaj   +1 more source

Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

Econometrics, 2019
Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is ...
Richard M. Golden, Steven S. Henley, Halbert White, T. Michael Kashner   +3 more
doaj   +1 more source

A new test for detecting specification errors in Gaussian linear mixed-effects models

AIMS Mathematics
Linear mixed-effects models (LMEMs) are widely used in medical, engineering, and social applications. The accurate specification of the covariance matrix structure within the error term is known to impact the estimation and inference procedures. Thus, it
Jairo A. Angel, Francisco M.M. Rocha, Jorge I. Vélez, Julio M. Singer   +3 more
doaj   +1 more source

Modeling Model Misspecification in Structural Equation Models

Stats, 2023
Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent.
Alexander Robitzsch
doaj   +1 more source

Inference in Dynamic Discrete Choice Problems under Local Misspecification

, 2018
Single-agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification.
Bugni, Federico A., Ura, Takuya
core   +1 more source

Target Matrix Estimators in Risk-Based Portfolios

Risks, 2018
Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix.
Marco Neffelli
doaj   +1 more source