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Estimating and evaluating treatment effect heterogeneity: A causal forests approach
Research & Politics, 2023 In this paper, we introduce the causal forests method (Athey et al., 2019) and illustrate how to apply it in social sciences to addressing treatment effect heterogeneity.
Li Zheng, Weiwen Yin
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An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data
Mathematics, 2022 For analyzing multiple events data, the illness death model is often used to investigate the covariate–response association for its easy and direct interpretation as well as the flexibility to accommodate the within-subject dependence.
Xifen Huang +4 more
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High Quantile Estimation and the Port Methodology
Revstat Statistical Journal, 2009 In many areas of application, a typical requirement is to estimate a high quantile χ1−p of probability 1−p, a value, high enough, so that the chance of an exceedance of that value is equal to p, small.
Lígia Henriques-Rodrigues +1 more
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Mathematics, 2022 We study the non-parametric estimation of partially linear generalized single-index functional models, where the systematic component of the model has a flexible functional semi-parametric form with a general link function.
Mohamed Alahiane +3 more
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Gaussian Semi‐parametric Estimation of Fractional Cointegration [PDF]
Journal of Time Series Analysis, 2003 Abstract. We analyse consistent estimation of the memory parameters of a nonstationary fractionally cointegrated vector time series. Assuming that the cointegrating relationship has substantially less memory than the observed series, we show that a multi‐variate Gaussian semi‐parametric estimate, based on initial consistent estimates and possibly ...
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Direct Reduction of Bias of the Classical Hill Estimator
Revstat Statistical Journal, 2005 In this paper we are interested in an adequate estimation of the dominant component of the bias of Hill’s estimator of a positive tail index γ, in order to remove it from the classical Hill estimator in different asymptotically equivalent ways.
Frederico Caeiro +2 more
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Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
Mathematics, 2022 This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part ...
Aifen Feng +3 more
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Non- and semi-parametric estimation in models with unknown smoothness [PDF]
Economics Letters, 2006 Abstract Many asymptotic results for kernel-based estimators were established under some smoothness assumption on density. We propose a combined estimator that could lead to the best available rate without knowledge of density smoothness. A Monte Carlo example confirms its good performance.
Zinde-Walsh, Victoria, Kotlyarova, Yulia
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Journal of Causal Inference, 2023 There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite ...
Rudolph Kara E. +4 more
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Semi-Parametric Estimation in Failure Time Mixture Models [PDF]
Biometrics, 1995 A mixture model is an attractive approach for analyzing failure time data in which there are thought to be two groups of subjects, those who could eventually develop the endpoint and those who could not develop the endpoint. The proposed model is a semi-parametric generalization of the mixture model of Farewell (1982).
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