Results 61 to 70 of about 2,902,778 (234)
Variable selection in semiparametric regression modeling
, 2008 In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of ...
Li, Runze, Liang, Hua
core +2 more sources
Semiparametric inference for the recurrent event process by means of a single-index model [PDF]
, 2014 In this paper, we introduce new parametric and semiparametric regression techniques for a recurrent event process subject to random right censoring. We develop models for the cumula- tive mean function and provide asymptotically normal estimators.
Bouaziz, Olivier +2 more
core +5 more sources
Media Statistika, 2022 The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables.
Hesikumalasari Hesikumalasari +3 more
doaj +1 more source
Hidden Markov graphical models with state‐dependent generalized hyperbolic distributions
Canadian Journal of Statistics, EarlyView.Abstract In this article, we develop a novel hidden Markov graphical model to investigate time‐varying interconnectedness between different financial markets. To identify conditional correlation structures under varying market conditions and accommodate shape features embedded in financial time series, we rely upon the generalized hyperbolic family of ...
Beatrice Foroni +2 more
wiley +1 more source
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Journal of Probability and Statistics, 2009 Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions.
Risa Kato, Takayuki Shiohama
doaj +1 more source
Bayesian semiparametric additive quantile regression [PDF]
Statistical Modelling, 2013 Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as
Yue, Yu Ryan +4 more
openaire +3 more sources
Journal of Forecasting, EarlyView.ABSTRACT Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations.
Pietro Giorgio Lovaglio +1 more
wiley +1 more source
Comparison of parametric and semiparametric survival regression models with kernel estimation
, 2021 The modelling of censored survival data is based on different estimations of the conditional hazard function. When survival time follows a known distribution, parametric models are useful.
I. Selingerová, S. Katina, I. Horová
semanticscholar +1 more source
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
Joint Estimation and Bandwidth Selection in Partially Parametric Models
Journal of Applied Econometrics, EarlyView.ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown ...
Daniel J. Henderson +2 more
wiley +1 more source