Results 51 to 60 of about 19,219 (292)
Journal of Applied Econometrics, EarlyView.ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates.
Daniele Ballinari, Nora Bearth
wiley +1 more source
Semiparametric Bayesian measurement error modeling
Journal of Multivariate Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
María Paz Casanova +4 more
openaire +4 more sources
Mathematics, 2020 Geostatistical interpolation methods, sometimes referred to as kriging, have been proven effective and efficient for the estimation of target quantity at ungauged sites.
Sompop Moonchai, Nawinda Chutsagulprom
doaj +1 more source
ETAS Space–Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results
Applied Sciences, 2021 Chilean seismic activity is one of the strongest in the world. As already shown in previous papers, seismic activity can be usefully described by a space–time branching process, such as the ETAS (Epidemic Type Aftershock Sequences) model, which is a ...
Marcello Chiodi +4 more
doaj +1 more source
Computing Nonparametric Functional Estimates in Semiparametric Problems [PDF]
, 1992 The purpose of this note is to provide a brief account of available FORTRAN Routines for computing nonparametric functional estimates, Frequently used in semiparametric problems, evaluated at each data point. Then semiparametric estimates can be computed
Delgado, Miguel A.
core +1 more source
Mapping Causal Biology: Mendelian Randomization in the Era of Big Data
Med Research, EarlyView.Mendelian randomization (MR) leverages genetic variants to mitigate confounding biases in causal inference. This review systematically maps MR's methodological evolution, highlights its expanding applications in epidemiology and drug target validation, and outlines future directions for overcoming current biases through dynamic, multi‐omics, and cross ...
Xuanlu Shen +10 more
wiley +1 more source
Generalized Semiparametrically Structured Ordinal Models [PDF]
Biometrics, 2003 SummarySemiparametrically structured models are defined as a class of models for which the predictors may contain parametric parts, additive parts of covariates with an unspecified functional form, and interactions which are described as varying coefficients.
openaire +3 more sources
A More Accurate Estimation of Semiparametric Logistic Regression
Mathematics, 2021 Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance.
Xia Zheng +3 more
doaj +1 more source
Nonparametric and semiparametric estimation with discrete regressors [PDF]
, 1994 This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems.
Delgado, Miguel A., Mora, Juan
core
Local Eviction Moratoria and the Spread of COVID‐19
Southern Economic Journal, EarlyView.ABSTRACT At different stages during the initial onset of the COVID‐19 pandemic, various US states and local municipalities enacted eviction moratoria. One of the main aims of these moratoria was to slow the spread of COVID‐19 infections. We deploy a semiparametric difference‐in‐differences approach with an event study specification to examine whether ...
Julia Hatamyar, Christopher F. Parmeter
wiley +1 more source