Concave regression: value-constrained estimation and likelihood ratio-based inference

Constrained Nonparametric Kernel Regression: Estimation and Inference

Abstract. Restricted kernel regression methods have recently received much well-deserved attention. Powerful methods have been proposed for imposing monotonicity on the resulting estimate, a condition often dictated by theoretical concerns; see Hall, Huang, Gifford & Gijbels (2001) and Hall & Huang (2001), among others. However, to the best of our knowledge, there does not exist a simple yet ge...

Empirical Likelihood Based Rank Regression Inference

Communications in Statistics Simulation and Computation Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713597237 Empirical Likelihood Based Rank Regression Inference Ellen E. Bishop a; Yichuan Zhao b a Research Triangle Institute, International, Atlanta, Georgia, USA b Department of Mathematics and Statistics...

Maximum Likelihood Estimation and Likelihood-ratio Tests

The method of maximum likelihood (ML), introduced by Fisher (1921), is widely used in human and quantitative genetics and we draw upon this approach throughout the book, especially in Chapters 13–16 (mixture distributions) and 26–27 (variance component estimation). Weir (1996) gives a useful introduction with genetic applications, while Kendall and Stuart (1979) and Edwards (1992) provide more ...

Copula-Based Regression Estimation and Inference

In this paper we investigate a new approach of estimating a regression function based on copulas. The main idea behind this approach is to write the regression function in terms of a copula and marginal distributions. Once the copula and the marginal distributions are estimated we use the plug-in method to construct the new estimator. Because various methods are available in the literature for ...

Semiparametric Likelihood Ratio Inference Revisited