Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro

Displaying similar documents to “Estimation of nuisance parameters for inference based on least absolute deviations”

One Bootstrap suffices to generate sharp uniform bounds in functional estimation

Paul Deheuvels (2011)

Kybernetika

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We consider, in the framework of multidimensional observations, nonparametric functional estimators, which include, as special cases, the Akaike–Parzen–Rosenblatt kernel density estimators ([1, 18, 20]), and the Nadaraya–Watson kernel regression estimators ([16, 22]). We evaluate the sup-norm, over a given set $𝐈$ , of the difference between the estimator and a non-random functional centering factor (which reduces to the estimator mean for kernel density estimation). We show that, under...

Least empirical risk procedures in statistical inference

Wojciech Niemiro (1993)

Applicationes Mathematicae

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We consider the empirical risk function $Q_{n} (α) = \frac{1}{n} \sum_{i = 1}^{n} \cdot f (α, Z_{i})$ (for iid $Z_{i}$ ’s) under the assumption that f(α,z) is convex with respect to α. Asymptotics of the minimum of $Q_{n} (α)$ is investigated. Tests for linear hypotheses are derived. Our results generalize some of those concerning LAD estimators and related tests.

Robust estimation in capital asset pricing model.

Wong, Wing-Keung, Bian, Guorui (2000)

Journal of Applied Mathematics and Decision Sciences

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Estimation and prediction in regression models with random explanatory variables

Nguyen Bac-Van

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The regression model X(t),Y(t);t=1,...,n with random explanatory variable X is transformed by prescribing a partition $S_{1}, . . ., S_{k}$ of the given domain S of X-values and specifying $X (1), . . ., X (n) \cap S_{i} = X_{i 1}, . . ., X_{i α (i)}, i = 1, . . ., k .$ Through the conditioning $α (i) = a (i), i = 1, . . ., k, X_{i 1}, . . ., X_{i α (i)}; i = 1, . . ., k = x_{11}, . . ., x_{k a (k)}$ the initial model with i.i.d. pairs (X(t),Y(t)),t=1,...,n, becomes a conditional fixed-design $(x_{11}, . . ., x_{k a (k)})$ model $Y_{i j}, i = 1, . . ., k; j = 1, . . ., a (i)$ where the response variables $Y_{i j}$ are independent and distributed according to the mixed conditional distribution $Q (\cdot, x_{i j})$ of Y given X at the observed value $x_{i j}$ .Afterwards, we investigate the case $(Q) E (Y^{'} | x) = \sum_{i = 1}^{k} b_{i} (x) θ_{i} I_{S_{i}} (x), (Q) D (Y | x) = \sum_{i = 1}^{k} d_{i} (x) Σ_{i} I_{S_{i}} (x)$ which...

Minimax Prediction for the Multinomial and Multivariate Hypergeometric Distributions

Alicja Jokiel-Rokita (1998)

Applicationes Mathematicae

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A problem of minimax prediction for the multinomial and multivariate hypergeometric distribution is considered. A class of minimax predictors is determined for estimating linear combinations of the unknown parameter and the random variable having the multinomial or the multivariate hypergeometric distribution.

A central limit theorem for random fields.

Fazekas, István, Chuprunov, Alexey (2004)

Acta Mathematica Academiae Paedagogicae Nyí regyháziensis. New Series [electronic only]

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Bayesian nonparametric estimation of hazard rate in monotone Aalen model

Jana Timková (2014)

Kybernetika

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This text describes a method of estimating the hazard rate of survival data following monotone Aalen regression model. The proposed approach is based on techniques which were introduced by Arjas and Gasbarra [4]. The unknown functional parameters are assumed to be a priori piecewise constant on intervals of varying count and size. The estimates are obtained with the aid of the Gibbs sampler and its variants. The performance of the method is explored by simulations. The results indicate...

Bias correction on censored least squares regression models

Jesus Orbe, Vicente Núñez-Antón (2012)

Kybernetika

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This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. 45 (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow...