Zobrazeno 1 - 10
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pro vyhledávání: '"Müller, Ursula U."'
Autor:
Dai, Guorong, Müller, Ursula U.
This article considers a linear model in a high dimensional data scenario. We propose a process which uses multiple loss functions both to select relevant predictors and to estimate parameters, and study its asymptotic properties. Variable selection
Externí odkaz:
http://arxiv.org/abs/2006.16361
This article deals with the analysis of high dimensional data that come from multiple sources (experiments) and thus have different possibly correlated responses, but share the same set of predictors. The measurements of the predictors may be differe
Externí odkaz:
http://arxiv.org/abs/2006.16357
We construct an efficient estimator for the error distribution function of the nonparametric regression model Y = r(Z) + e. Our estimator is a kernel smoothed empirical distribution function based on residuals from an under-smoothed local quadratic s
Externí odkaz:
http://arxiv.org/abs/1810.01645
Akademický článek
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Autor:
Chown, Justin, Müller, Ursula U.
Heteroskedastic errors can lead to inaccurate statistical conclusions if they are not properly handled. We introduce a test for heteroskedasticity for the nonparametric regression model with multiple covariates. It is based on a suitable residual-bas
Externí odkaz:
http://arxiv.org/abs/1610.09139
Autor:
Chown, Justin, Müller, Ursula U.
Publikováno v:
Chown, J. and M\"uller, U.U. (2013). Efficiently estimating the error distribution in nonparametric regression with responses missing at random. J. Nonparametr. Stat. 25, 665-677
This article considers nonparametric regression models with multivariate covariates and with responses missing at random. We estimate the regression function with a local polynomial smoother. The residual-based empirical distribution function that on
Externí odkaz:
http://arxiv.org/abs/1610.08360
Autor:
Chown, Justin, Müller, Ursula U.
Publikováno v:
Journal of the Royal Statistical Society. Series B (Statistical Methodology), 2019 Jan 01. 81(4), 805-806.
Externí odkaz:
https://www.jstor.org/stable/26773235
Publikováno v:
Annals of Statistics 2012, Vol. 40, No. 6, 3031-3049
This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptot
Externí odkaz:
http://arxiv.org/abs/1302.4605
Autor:
Müller, Ursula U.
Publikováno v:
Annals of Statistics 2009, Vol. 37, No. 5A, 2245-2277
We consider regression models with parametric (linear or nonlinear) regression function and allow responses to be ``missing at random.'' We assume that the errors have mean zero and are independent of the covariates. In order to estimate expectations
Externí odkaz:
http://arxiv.org/abs/0908.3102
Autor:
Chown, Justin, Müller, Ursula U.
Publikováno v:
Journal of the Royal Statistical Society. Series B (Statistical Methodology), 2018 Jan 01. 80(5), 951-974.
Externí odkaz:
https://www.jstor.org/stable/26773188