Results 41 to 50 of about 5,713 (196)
Bayesian semiparametric regression models to characterize molecular evolution
Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties.
Datta Saheli +2 more
doaj +1 more source
Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and ...
Xiangyi Zheng +5 more
doaj +1 more source
Semiparametric mixtures of regressions [PDF]
We present an algorithm for estimating parameters in a mixture-of-regressions model in which the errors are assumed to be independent and identically distributed but no other assumption is made. This model is introduced as one of several recent generalizations of the standard fully parametric mixture of linear regressions in the literature.
David R. Hunter, Derek S. Young
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Mapping Causal Biology: Mendelian Randomization in the Era of Big Data
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
Local Eviction Moratoria and the Spread of COVID‐19
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
Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification
Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β (
Ying-Ying Lee
doaj +1 more source
Regulation, Taxation, and Resources: Unpacking Greenhouse Gas Emission Drivers Across G7 Economies
ABSTRACT Advanced economies are under growing pressure to downscale greenhouse gas (GHG) emissions without undermining growth, yet G7 (Group of Seven) nations, representing almost 10% of the world's population, still generate one quarter of global GHGs.
Mohammad Imtiaz Hossain +5 more
wiley +1 more source
Semiparametric regression model approach is a model approach that combines parametric regression models and nonparametric regression. On semiparametric regression, most explanatory variables are parametric and nonparametric others are.
ANNA FITRIANI +2 more
doaj
Quantifying the effects of passenger-level heterogeneity on transit journey times
In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems.
Ramandeep Singh +2 more
doaj +1 more source
Semiparametric Regression in Size‐Biased Sampling [PDF]
SummarySize‐biased sampling arises when a positive‐valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size‐biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of ...
openaire +3 more sources

