A large sample study of nonparametric proportional hazard regression model

Petr Volf

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Model selection for (auto-)regression with dependent data

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In this paper, we study the problem of non parametric estimation of an unknown regression function from dependent data with sub-gaussian errors. As a particular case, we handle the autoregressive framework. For this purpose, we consider a collection of finite dimensional linear spaces (e.g. linear spaces spanned by wavelets or piecewise polynomials on a possibly irregular grid) and we estimate the regression function by a least-squares estimator built on a data driven selected linear...

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A nonparametric method of regression analysis from censored data

Petr Volf (1988)

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Large adaptive estimation in linear regression model. I. Consistency

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The least trimmed squares. Part III: Asymptotic normality

Jan Ámos Víšek (2006)

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Asymptotic normality of the least trimmed squares estimator is proved under general conditions. At the end of paper a discussion of applicability of the estimator (including the discussion of algorithm for its evaluation) is offered.