Parameter estimation for uniform maximum process.

Ristić, Miroslav; Popović, Biljana Č.

Displaying similar documents to “Parameter estimation for uniform maximum process.”

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...

Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro (1995)

Applicationes Mathematicae

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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.

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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Robust estimation in capital asset pricing model.

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

Journal of Applied Mathematics and Decision Sciences

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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.

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...

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...

Some measures of the amount of information for the linear regression model.

Mihoc, Ion, Fătu, Cristina–Ioana (2008)

Acta Universitatis Apulensis. Mathematics - Informatics

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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.

A note on the characterization ofsome minification processes

Wiesław Dziubdziela (1997)

Applicationes Mathematicae

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We present a stochastic model which yields a stationary Markov process whose invariant distribution is maximum stable with respect to the geometrically distributed sample size. In particular, we obtain the autoregressive Pareto processes and the autoregressive logistic processes introduced earlier by Yeh et al