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EN
Generalized variance i.e. the determinant of the covariance matrix is a scalar measure of multivariate distribution dispersion. The exact distribution of the generalized variance is known only for multivariate normal vectors. For random vectors in high dimensional spaces it has a complicated formula very troublesome to apply. An estimator of the logarithm of generalized variance derived with the help of limit theorems for random determinants was presented as well as its properties in examples of chosen simulation multivariate distributions.
PL
Uogólniona wariancja, czyli wyznacznik macierzy kowariancji jest skalarną miarą rozrzutu rozkładów wielowymiarowych. Dokładny rozkład uogólnionej wariancji znany jest tylko dla wektorów losowych o wielowymiarowym rozkładzie normalnym. Dla wektorów losowych o dużych wymiarach przyjmuje on skomplikowaną postać, co stanowi utrudnienie w zastosowaniach praktycznych. W pracy przedstawiono tzw. G-estymator logarytmu uogólnionej wariancji otrzymany na podstawie twierdzeń granicznych dla wyznaczników losowych i jego własności na przykładach symulacyjnych dla kilku wybranych rozkładów wielowymiarowych.
EN
The trend of time series can change its direction. It is assumed that the time interval is divided into subintervals where the trend is given as particular linear function. The problem is how to divide the observation of time series into disjoint and coherent groups where they have linear trend. That is why the problem of the scatter of multivariable observation was first considered. The degree of data spread is measured by means of a coefficient called a discriminant of multivariable observation. It is equal to the sum of volumes of the parallelotops spanned on multidimensional observations. On the basis of it the modifications of the well known generalized variance were introduced. Geometrical properties of those parameters were investigated. The obtained results are used to generalize well-known clustering methods of Ward. One of the advantages of the method is that it finds clusters of high linear dependent multivariate observations. Finally, the results are used to partition a time series into homogeneous groups where observations are close to linear trend. There is considered an example.
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