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pro vyhledávání: '"Jacek Koronacki"'
Autor:
Jacek Koronacki, Jan Ćwik
Publikováno v:
Computational Statistics & Data Analysis. 26:199-218
A multivariate extension of the plug-in kernel (and filtered kernel) estimator is proposed and this uses asymptotically optimal bandwidth matrix (matrices) for a normal mixture approximation of a density to be estimated (the filtered kernel estimator
Autor:
Jacek Koronacki, Jan Ćwik
Publikováno v:
Neural Computing & Applications. 6:173-185
This paper is a continuation of the authors' earlier work [1], where a version of the Traven's [2] Gaussian clustering neural network (being a recursive counterpart of the EM algorithm) has been investigated. A comparative simulation study of the Gau
Autor:
Jacek Koronacki, Jan Ćwik
Publikováno v:
Neural Computing & Applications. 4:149-160
A version of the Traven's [1] Gaussian clustering algorithm for normal mixture densities is studied. Unlike in the case of the Traven's algorithm, no constraints on covariance structure of mixture components are imposed. Simulations suggest that the
Autor:
Jacek Koronacki, U. Luboińska
Publikováno v:
Computational Statistics & Data Analysis. 18:317-330
Let X (1) ,..., X (m) be m independent, real-valued random variables with unknown densities and let Y = Ψ(X (1) ,...,X (m) ) be their known functional. The aim is to estimate the density of the random variable Y , given n independent realizations of
Autor:
Jacek Koronacki, W. Feluch
Publikováno v:
Computational Statistics & Data Analysis. 13:143-151
In the note, a simple modification of the least-squares cross-validation criterion is proposed for choosing the bandwidth of kernel density estimators. For an i.i.d. sample X1, X2, …, Xn, the modification consists in leaving out a number of terms w