Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/11884
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dc.contributor.authorChaudhuri, Probal-
dc.date.accessioned2014-04-23T06:03:43Z-
dc.date.available2014-04-23T06:03:43Z-
dc.date.issued2011-11-17-
dc.identifier.urihttp://hdl.handle.net/11718/11884-
dc.descriptionThe seminar on R & P held at Wing 11 IIM Ahmedabad on 24/07/2012 by Prof. Probal Chaudhuri, Indian Statistical Institute, Kolkata.en_US
dc.description.abstractThe need for comparing two or more regression functions arises frequently in statistical applications. Comparison of the usual regression functions is not very meaningful in situations where the distribution and the range of the covariates have changed for the populations. For instance, in econometric studies, the prices of commodities and people’s incomes observed at different time points may not be on comparable scales due to inflation and other economic factors. In this paper we describe, motivated by an idea of Mahalanobis (1960), a method of standardizing the covariates and estimating the transformed regression function, which now become comparable. We develop smooth estimates of fractile regression function and study its statistical properties. We prove the consistency and asymptotic normality of the estimated fractile regression function defined through general weight functions. We illustrate our method through analysis of three data sets: blood pressure and related measurements of two tribes in India, profit and sales of private companies in India at two time points, and data on household income and expenditure of two East European transitional economies.en_US
dc.language.isoen_USen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectConsistency and asymptotic normalityen_US
dc.subjectGeometric quantileen_US
dc.subjectKernel smoothingen_US
dc.subjectMultivariate fractileen_US
dc.subjectSmooth estimatesen_US
dc.subjectTransformation of covariatesen_US
dc.titleOn fractile regressionen_US
dc.typeVideoen_US
Appears in Collections:R & P Seminar

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Part_1.mp4On fractile regression Part 12.56 MBMP4 VideoView/Open
Part_2.mp4On fractile regression Part 2120.92 MBMP4 VideoView/Open
Part_3.mp4On fractile regression Part 343.79 MBMP4 VideoView/Open


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