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Partially Linear Model
A partially linear model is a form of semiparametric model In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components. A statistical model is a parameterized family of distributions: \ indexed by a parameter \theta. * A parametric model is a model in ..., since it contains parametric and nonparametric elements. Application of the least squares estimators is available to partially linear model, if the hypothesis of the known of nonparametric element is valid. Partially linear equations were first used in the analysis of the relationship between temperature and usage of electricity by Engle, Granger, Rice and Weiss (1986). Typical application of partially linear model in the field of Microeconomics is presented by Tripathi in the case of profitability of firm's production in 1997. Also, partially linear model applied successfully in some other academic field. In 1994, Zeger and Diggle introduced partially linear model into bio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semiparametric Model
In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components. A statistical model is a parameterized family of distributions: \ indexed by a parameter \theta. * A parametric model is a model in which the indexing parameter \theta is a vector in k-dimensional Euclidean space, for some nonnegative integer k.. Thus, \theta is finite-dimensional, and \Theta \subseteq \mathbb^k. * With a nonparametric model, the set of possible values of the parameter \theta is a subset of some space V, which is not necessarily finite-dimensional. For example, we might consider the set of all distributions with mean 0. Such spaces are vector spaces with topological structure, but may not be finite-dimensional as vector spaces. Thus, \Theta \subseteq V for some possibly infinite-dimensional space V. * With a semiparametric model, the parameter has both a finite-dimensional component and an infinite-dimensional component (often a real-valued functio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Babette Brumback
Babette Anne Brumback is an American biostatistician known for her work on causal inference. She is a professor of biostatistics at the University of Florida. Education and career Brumback earned a bachelor's degree in electrical engineering at the University of Virginia in 1988. She went to the University of California, Berkeley for graduate study, originally in electrical engineering and computer science but then switching to statistics; she earned a master's degree in 1992 and completed her Ph.D. in 1996. Her dissertation, ''Statistical Methods for Hormone Data'', was supervised by John A. Rice. After postdoctoral research at Harvard University she became an assistant professor of biostatistics at the University of Washington in 1999, and while there also became affiliated with the Fred Hutchinson Cancer Research Center. She moved to the University of California, Los Angeles in 2002 and again to the University of Florida in 2004. Honors and awards Brumback chaired the Stati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |