Results 161 to 170 of about 2,000,076 (197)

Analytical Description of Empirical Probability Distribution Functions

Russian Engineering Research, 2020
The selection of an analytical expression approximating an empirical probability distribution function is considered. For specific examples, the problems that arise in the analysis of data from simulations and tests of aerospace products are identified. These problems cannot be solved by classical statistical methods.
P. A. Iosifov, A. V. Kirillin
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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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