Results 51 to 60 of about 15,261 (244)
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
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
Endogeneity in semiparametric binary response models [PDF]
, 2003 This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single index binary response models to the ...
Blundell, R., Powell, J.L.
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JTAM (Jurnal Teori dan Aplikasi Matematika)Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression.
Tiani Wahyu Utami +2 more
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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
A semiparametric factor model for electricity forward curve dynamics [PDF]
In this paper we introduce the dynamic semiparametric factor model (DSFM) for electricity forward curves. The biggest advantage of our approach is that it not only leads to smooth, seasonal forward curves extracted from exchange traded futures and ...
Weron, Rafal, Borak, Szymon
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International Journal for Re-Views in Empirical Economics, 2019 This paper aims to replicate the semiparametric Value-At-Risk model by Dias (2014) and to test its legitimacy. The study confirms the superiority of semiparametric estimation over classical methods such as mixture normal and Student-t approximations in ...
Jiahua Xu
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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
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Teaching Statistics, EarlyView.Abstract Statistical hypothesis testing (SHT) is widely employed across numerous scientific disciplines, and a clear understanding of its underlying logic is essential for the broader scientific community. Here, drawing upon both epistemological and statistical perspectives, we aim to clarify—primarily for educational purposes—the logical relationship ...
Maria Cristina Amoretti +1 more
wiley +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
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