Local Identification of Nonparametric and Semiparametric Models by Chen, Xiaohong, Chernozhukov, Victor V., Lee, Sokbae, Newey, Whitney K.

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Bayesian quantile regression for semiparametric models by Hu, Yuao

“…However, there has been relatively less work focusing on quantile regression for nonparametric models or semiparametric models, especially from a Bayesian perspective. …”
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Variable selection in parametric and semiparametric models by Lin, Bingqing

“…There are relatively less papers discussing model selection problems in some special but important parametric and semiparametric models, such as linear mixed-effects model, partially varying-coefficient single-index model and single-index-coefficient regression model. …”
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Estimation of system reliability using a semiparametric model by Wu, Leon, Teravainen, Timothy, Kaiser, Gail, Anderson, Roger, Boulanger, Albert, Rudin, Cynthia

“…This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). …”
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GEE-smoothing spline in semiparametric model with correlated nominal data by Ibrahim, Noor Akma, Suliadi

“…In this paper we propose GEE‐Smoothing spline in the estimation of semiparametric models with correlated nominal data. The method can be seen as an extension of parametric generalized estimating equation to semiparametric models. …”
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Semiparametric latent factor models by Teh, Y, Seeger, M, Jordan, M

“…We propose a semiparametric model for regression problems involving multiple response variables. …”

Efficient semiparametric estimation and model selection for multidimensional mixtures by Gassiat, E, Rousseau, J, Vernet, E

“…We approximate the semiparametric model by projecting the conditional distributions on step functions associated to some partition. …”

Quasi-centralized limit order books by Howison, S, Gould, M, Porter, M

“…Motivated by this finding, we propose a semiparametric model of order flow and LOB state for a single trading day. …”

Rank regression for modeling bus dwell time in the presence of censored observations by Karimi, Mostafa, Ibrahim, Noor Akma

“…Rank regression based on the accelerated failure time model is a semiparametric model that does not involve assumptions about the model variables or the model error terms. …”
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Testing for causal e ffects in a generalized regression model with endogenous regressors by Abrevaya, Jason, Hausman, Jerry A., Khan, Shakeeb

“…To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous regressors, (ii) endogenous regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary “mix” of endogenous regressors (e.g., one binary regressor and one continuous regressor).…”
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Regression analysis of survival data with covariates subject to censoring by Yu, Tonghui

“…We propose a new semiparametric model for the terminal event time based on proportional hazards regression conditioning on the non-terminal event time. …”
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Gee-Smoothing Spline for Semiparametric Estimation of Longitudinal Categorical Data by Suliadi

“…We develop GEE-Smoothing spline as a method to analyze semiparametric model for longitudinal data. The proposed methods are an extension of parametric generalized estimating equation (GEE) to semiparametric GEE by introducing smoothing spline into parametric GEE. …”
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Measuring the effects of online advertising on human behavior using natural and field experiments by Lewis, Randall A. (Randall Aaron)

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Efficient algorithms for Bayesian semi-parametric regression models by Zhao Kaifeng

“…Semiparametric models have played an increasingly important role in statistical research and received much attention in both frequentist and Bayesian contexts. …”
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Shrinkage estimation for identification of linear components in additive models by Lian, Heng

“…In this short paper, we demonstrate that the popular penalized estimation method typically used for variable selection in parametric or semiparametric models can actually provide a way to identify linear components in additive models. …”
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Average and Quantile Effects in Nonseparable Panel Models by Fernandez-Val, Ivan, Hahn, Jinyong, Chernozhukov, Victor V, Newey, Whitney K

“…Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. …”
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Variable selection in high-dimensional partly linear additive models by Lian, Heng

“…Semiparametric models are particularly useful for high-dimensional regression problems. …”
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Estimation by polynomial splines with variable selection in additive Cox models by Zhang, Shangli, Wang, Lichun, Lian, Heng

“…In this article, we consider penalized variable selection in additive Cox models based on (group) smoothly clipped absolute deviation penalty and hence widen the scope of applicability of penalized variable selection to semiparametric models for censored data.We demonstrate the asymptotic consistency in model selection and convergence rate in estimation. …”
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Testing structural change in partially linear single-index models with error-prone linear covariates by Huang, Zhensheng, Pang, Zhen, Hu, Tao

“…Based on the local linear estimation for the unknowns in these semiparametric models, we develop a new generalized F-test statistics for the nonparametric part in the partially linear single-index models with error-prone linear covariates. …”
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Semiparametric regression analysis of clustered survival data with semi-competing risks by Peng, Mengjiao, Xiang, Liming, Wang, Shanshan

“…To incorporate dependency within clusters and association between two types of event times, we propose a new flexible semiparametric modeling framework where a copula model is employed for the joint distribution of the nonterminal and terminal events, and their marginal distributions are modeled by Cox proportional hazards models with random effects. …”
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