Results 71 to 80 of about 5,885 (195)
Wavelet Threshold Estimator of Semiparametric Regression Function with Correlated Errors
پژوهشهای ریاضی, 2022 Wavelet analysis is one of the useful techniques in mathematics which is used much in statistics science recently. In this paper, in addition to introduce the wavelet transformation, the wavelet threshold estimation of semiparametric regression model ...
Mahmoud Afshari +2 more
doaj
Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
Partial Time‐Varying Regression Modelling Under General Heterogeneity
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper studies a semiparametric time‐varying regression model in which a subset of regressors is associated with fixed parameters, while the remaining regressors have parameters that evolve smoothly over time. We propose a closed‐form semiparametric Frisch‐Waugh‐Lovell estimator for the fixed parameters, and a non‐parametric kernel type ...
Liudas Giraitis +3 more
wiley +1 more source
Multivariate and semiparametric kernel regression [PDF]
, 1997 The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses.
Härdle, Wolfgang, Müller, Marlene
openaire +2 more sources
Exploring Spatial Patterns in Inefficiency Using a Stationary Gaussian Random Field
Oxford Bulletin of Economics and Statistics, EarlyView.ABSTRACT This article presents a semiparametric stochastic frontier model that accounts for unobserved spatial heterogeneity in inefficiency by incorporating a stationary Gaussian random field based on geographic coordinates into the inefficiency scaling function. This approach captures spatial patterns in inefficiency at a fine scale.
Ioannis Skevas
wiley +1 more source
Semiparametric Counterfactual Regression
We study counterfactual regression, which aims to map input features to outcomes under hypothetical scenarios that differ from those observed in the data. This is particularly useful for decision-making when adapting to sudden shifts in treatment patterns is essential.
openaire +2 more sources
StatsWe investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled parametrically and others nonparametrically. We propose a class of augmented inverse probability weighted (AIPW)
Lu Wang, Zhongzhe Ouyang, Xihong Lin
doaj +1 more source
Behavioral Charity: Third‐Party Ratings of Nonprofits as Salience and Heuristics
Public Administration Review, EarlyView.ABSTRACT Financial disclosure through tax returns is the primary regulatory mechanism for holding nonprofits accountable to donors in the USA. The assumption that donors will make rational decisions using disclosed information when giving to nonprofits is central to this regulation. But what if they rely on mental shortcuts instead? This study examines
Ashraf Haque
wiley +1 more source
BarekengSome semiparametric regression model approaches include spline, kernel, Fourier series, and wavelet. Semiparametric regression modelling can involve more than one independent variable (multivariable), a parametric approach is usually combined with one of
Lilik Hidayati +4 more
doaj +1 more source
Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting
Applied Computational Intelligence and Soft Computing, 2017 Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are ...
Jelena Fiosina, Maksims Fiosins
doaj +1 more source