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Showing 1 to 5 of 5 for “"semiparametric efficiency"”.

  1. Essays in semiparametric and nonparametric estimation with application to growth accounting

    This dissertation develops efficient semiparametric estimation of parameters and expectations in dynamic nonlinear systems and analyzes the role of environmental factors in productivity growth accounting. The first essay considers the estimation of a general class of dynamic nonlinear systems. The …

    rice Repository record for Essays in semiparametric and nonparametric estimation with application to growth accounting (opens in a new tab)

  2. Modern Methods in Semiparametric Statistics

    … of interest to look beyond parametric models. Semiparametric models, defined in terms of infinite dimensional parameters, provides extra flexibility over potentially misspecified parametric models. The desire to construct estimators for parameters of interest that are optimal over the broad …

    cambridge Repository record for Modern Methods in Semiparametric Statistics (opens in a new tab)

  3. Some Nonparametric Methods for Clinical Trials and High Dimensional Data

    … studies. It can be used to increase efficiency and thus power, and to reduce possible bias. While most statistical tests in randomized clinical trials are nonparametric in nature, approaches for covariate adjustment typically rely on specific regression models, such as the linear …

    columbia-diss Repository record for Some Nonparametric Methods for Clinical Trials and High Dimensional Data (opens in a new tab)

  4. Semiparametric Methods for Two Problems in Causal Inference using Machine Learning

    … black-box nature pose an inferential challenge. Semiparametric methods are able to nonetheless leverage these powerful nonparametric regression procedures to provide valid statistical analysis on interesting parametric components of the data generating process. This thesis consists of three …

    cambridge Repository record for Semiparametric Methods for Two Problems in Causal Inference using Machine Learning (opens in a new tab)

  5. Causal Inference with Survival Outcomes via Orthogonal Statistical Learning

    … Our approach combines importance sampling, semiparametric theory, and Neyman orthogonality to resolve both model misspecification and lack of covariate overlap between treatment arms in observational studies with censored outcomes. We give regularity conditions for the consistency, …

    mit Repository record for Causal Inference with Survival Outcomes via Orthogonal Statistical Learning (opens in a new tab)