Results 301 to 310 of about 2,000,720 (315)

Chung—Smirnov property for perturbed empirical distribution functions

Statistics & Probability Letters, 1993
The distribution function (DF) \(F\) is estimated by using the integral of a sequence of kernel density estimators. It converges to the \(df\) of the unit mass zero. The convergence of the derived estimator \(\hat F_ n\) is well known. The author obtains new convergence criteria.
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19 Empirical distribution function

1984
Publisher Summary This chapter describes the empirical distribution function. A statistical estimation of F(x) based on a random sample (X 1 . . . X n ,) is the so-called empirical or sample distribution function. F(x) is considered also a (random) function of x . To apply statistical methods based on empirical distribution, such as goodness of
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Jackknife empirical likelihood tests for distribution functions

Journal of Statistical Planning and Inference, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feng, Huijun, Peng, Liang
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FAST CORRECTION ALGORITHMS FOR WEIGHTED EMPIRICAL DISTRIBUTION FUNCTIONS

Advances and Applications in Statistics, 2019
Summary: The weighted empirical distribution functions are used as estimators for distributions of components in a mixture with varying concentrations. But the weight coefficients can be negative, therefore the weighted empirical distribution functions cannot be the probability distribution functions.
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Empirical Distribution Function in an Experiment withBinomial Randomization

Theory of Probability & Its Applications, 1997
The notion of empirical distribution function for the observations of a finite population during a randomized experiment, which was introduced by \textit{B. Rosén} [Ark. Mat. 5, 383-424 (1965; Zbl 0127.10503)] and \textit{Yu. K. Belyaev} [Probabilistic methods of sampling analysis.
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MINIMAXITY OF THE EMPIRICAL DISTRIBUTION FUNCTION IN DISCRETE INVARIANT ESTIMATION OF A DISTRIBUTION FUNCTION

Statistics & Risk Modeling, 1992
The problem of invariant estimation of a discrete probability distribution is considered. It is proved that the sample cumulative distribution function is minimax. Some modifications of the loss function are discussed.
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Minimax Prediction of the Empirical Distribution Function

Communications in Statistics - Theory and Methods, 2009
Let X 1,…, X n be i.i.d. random variables from an unknown cumulative distribution function F defined on the real line ℝ. No assumptions are made on the unknown F. The problem is to predict the empirical distribution function of a future sample Y = (Y 1,…,Y m ) from the distribution function F on the basis of the observations X 1,…, X n .
Alicja Jokiel-Rokita, Ryszard Magiera
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On some limit laws for perturbed empirical distribution functions

Statistics & Probability Letters, 1994
The convergence of random variables of the form \(S_ n = \int F_ n (T_ n - s) v_ n (ds)\) is established, where \(T_ n\) is some random variable, \(F_ n\) is an empirical distribution function based on an independent sample of size \(n\) and \(v_ n\) is some measure. As an example we state the following result.
Denker, Manfred, Puri, Madan L.
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Minmax estimation of the empirical distribution function

Mathematica Applicanda, 1981
Let X1,⋯,Xm and Y1,⋯,Yn be two independent samples from the same distribution. The problem is to predict the empirical distribution function, F^(t)=∑ni=1δ(Yi,t), from the second sample using the first sample, where δ(Yi,t)=1 if Yi≤t, and δ(Yi,t)=0 otherwise.
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Convergence rate of perturbed empirical distribution functions

Journal of Applied Probability, 1979
Given an i.i.d. sequenceX1,X2, … with common distribution function (d.f.) F, the usual non-parametric estimator ofFis the e.d.f.Fn;whereUois the d.f. of the unit mass at zero. Anadmissible perturbation of the e.d.f., say, is obtained ifUois replaced by a d.f., whereis a sequence of d.f.'s converging weakly toUo.Suchperturbed e.d.f.′s arise quite ...
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