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1
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Nonparametric estimation of a density function of several variables

100%
Krzyśko M.
Mathematica Applicanda
|
1977
|
tom 5
|
nr 11
PL
Artykuł nie zawiera streszczenia
EN
The article contains no abstract
2
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Methods of discriminant analysis

100%
Krzyśko M.
Mathematica Applicanda
|
1982
|
tom 10
|
nr 19
PL
Rozpatrzmy zadanie polegające na tym, by na podstawie zaobserwowanych wartości cech obiektu zaklasyfikować go do jednej z k populacji, których obiekt ten może być elementem. W zadaniach tego rodzaju problem polega na wyborze jednej z szeregu hipotez alternatywnych a nie na testowaniu jakiejkolwiek szczególnej hipotezy przeciwko zbiorowi alternatyw, jak to ma miejsce w klasycznej teorii testowania hipotez. W literaturze statystycznej zadania takie noszą nazwę zadań klasyfikacji, identyfikacji lub dyskryminacji. Praca zawiera przegląd podstawowych metod analizy dyskryminacyjnej: metody zmiennych dyskryminacyjnych pochodzącej od R. A. Fishera [12], metody teoriodecyzyjnej pochodzącej od A. Walda i T. W. Andersona [2], [3], [30] oraz metody bayesowskiej pochodzącej od S. Geissera
EN
The article contains no abstract
3
Content available remote

Canonical correlation analysis for functional data

64%
Krzyśko M., Waszak Ł.
Biometrical Letters
|
2013
|
tom 50
|
nr 2
95-105
EN
Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we propose a new method of constructing canonical correlations and canonical variables for a pair of stochastic processes represented by a finite number of orthonormal basis functions.
4
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
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A learning algorithm combining functional discriminant coordinates and functional principal components

64%
Górecki T., Krzyśko M.
Discussiones Mathematicae Probability and Statistics
|
2014
|
tom 34
|
nr 1-2
127-141
EN
A new type of discriminant space for functional data is presented, combining the advantages of a functional discriminant coordinate space and a functional principal component space. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 35 functional data sets (time series). Experiments show that constructed combined space provides a higher quality of classification of LDA method compared with component spaces.
5
Content available remote

A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

64%
Deręgowski K., Krzyśko M.
Biometrical Letters
|
2014
|
tom 51
|
nr 1
57-73
EN
Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC
6
Content available remote

Principal component analysis for functional data on grain yield of winter wheat cultivars

51%
Krzyśko M., Derejko A., Górecki T., Gacek E.
Biometrical Letters
|
2013
|
tom 50
|
nr 2
81-94
EN
The aim of this paper is to present a statistical methodology to assess patterns of cultivars' adaptive response to agricultural environments (agroecosystems) on the basis of complete Genotype x Crop Management x Location x Year (GxMxLxY) data obtained from 3-year multi-location twofactor trials conducted within the framework of the Polish post-registration trials (PDOiR), with an illustration of the application and usefulness of this methodology in analyzing winter wheat grain yield. Producing specific varieties for each subregion of a target region, from widely adapted varieties, may exploit positive genotype x location (GL) interactions to increase crop yields. Experiments designed to examine combinations of environment (E), management practices (M) and cultivars (G) also provide evidence of the relative importance of each of these factors for yield improvement. The evidence shows that variation due to E far outweighs the variation of grain yield that can be attributed to M or G, or the interactions between these factors, and between these factors and E (Anderson, 2010). This statistical method involves the use of functional PCA and cluster analysis. A total of 24 cultivars were evaluated over 3 years in 20 environments using randomized incomplete split-block designs with two replications per trial. The methodology proved an efficient tool for the reliable classification of 24 winter wheat cultivars, distinguishing cultivar groups that exhibited homogeneous adaptive response to environments. It enables the identification of cultivars displaying wide or specific adaptation. The remaining cultivars were locally adapted to some testing environments, or some of them were not relatively adapted to the environments because they always yielded substantially below the environmental means. Performing earlier specific selection, or adopting distinct genetic bases for each agro-ecosystem, may further increase the advantage of specific breeding.
7
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The mineralization effect of wheat straw on soil properties described by MFPC analysis and other methods

51%
Jakubus M., Krzyśko M., Wołyński W., Graczyk M.
Biometrical Letters
|
2016
|
tom 53
|
nr 2
133-147
EN
Recycling of crop residues is essential to sustain soil fertility and crop production. Despite the positive effect of straw incorporation, the slow decomposition of that organic substance is a serious issue. The aim of the study was to assess the influence of winter wheat straws with different degrees of stem solidness on the rate of decomposition and soil properties. An incubation experiment lasting 425 days was carried out in controlled conditions. To perform analyses, soil samples were collected after 7, 14, 21, 28, 35, 49, 63, 77, 91, 119, 147, 175, 203, 231, 259, 313, 341, 369, 397 and 425 days of incubation. The addition of two types of winter wheat straw with different degree of stem solidness into the sandy soil differentiated the experimental treatments. The results demonstrate that straw mineralization was a relatively slow process and did not depend on the degree of filling of the stem by pith. Multivariate functional principal component analysis (MFPC) gave proof of significant variation between the control soil and the soil incubated with the straws. The first functional principal component describes 48.53% and the second 18.55%, of the variability of soil properties. Organic carbon, mineral nitrogen and sum of bases impact on the first functional principal component, whereas, magnesium, sum of bases and total nitrogen impact on the second functional principal component.
8
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Classifiers for doubly multivariate data

51%
Krzyśko M., Skorzybut M., Wołyński W.
Discussiones Mathematicae Probability and Statistics
|
2011
|
tom 31
|
nr 1-2
5-27
EN
This paper proposes new classifiers under the assumption of multivariate normality for multivariate repeated measures data (doubly multivariate data) with Kronecker product covariance structures. These classifiers are especially useful when the number of observations is not large enough to estimate the covariance matrices, and thus the traditional classifiers fail. The quality of these new classifiers is examined on some real data. Computational schemes for maximum likelihood estimates of required class parameters, and the likelihood ratio test relating to the structure of the covariance matrices, are also given.
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