Lina Dreižienė

Lina Dreižienė
Klaipeda University · Informatics and Statistics

PhD

About

19
Publications
603
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34
Citations
Introduction
Spatial statistics, discriminant analysis
Additional affiliations
September 2016 - present
Lithuanian maritime academy
Position
  • Asoc. Professor

Publications

Publications (19)
Article
Bayes multiclass classification of spatial Gaussian data following the universal kriging model is considered. The closed-form expressions for the maximum likelihood (ML) estimator of regression parameters and the actual error rate (AER) in terms of semivariograms are derived.
Article
Full-text available
The novel approach for classifying spatial Gaussian data based on Bayes discriminant functions in terms of semivariograms has been developed. We derived the closed-form expressions for the Maximum Likelihood estimator of regression parameters and the actual error rate (AER). Results are illustrated through simulation study of spatial Gaussian data...
Article
Full-text available
Paper deals with statistical classification of spatial data as a part of widely applicable statistical approach to pattern recognition. Error rates in supervised classification of Gaussian random field observation into one of two populations specified by different constant means and common stationary geometric anisotropic covariance are considered....
Article
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Discrimination and classification of spatial data has been widely mentioned in the scientific literature, but lacks full mathematical treatment and easily available algorithms and software. This paper fills this gap by introducing the method of statistical classification based on Bayes discriminant function (BDF) and by providing original approach...
Article
In this paper, a novel approach to comparison and selection of spatial linear mixed models based on a hybrid estimator of actual correct classification rates is considered. We focus on the method of statistical classification based on Bayes discriminant function (BDF) by providing modified well-known methods for estimation of correct classification...
Article
Full-text available
Given the spatial lattice endowed with particular neighborhood structure, the problem of classifying a scalar Gaussian Markov random field (GMRF) observation into one of two populations specified by different regression coefficients and special parametric covariance (precision) matrix is considered. Classification rule based on the plug-in Bayes di...
Article
The problem of supervised classifying of a multivariate Gaussian Markov random field (GMRF) observation into one of two populations specified by different regression mean models is considered. We focus on a multivariate conditionally autoregressive model, a subclass of GMRF with parametrical structure proposed by Pettitt et al. (2002) and generaliz...
Article
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The problem of classifying a spatial multivariate Gaussian data into one of several categories specified by different regression mean models is considered. The classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators is investigated. Th...
Article
Given training sample, the problem of classifying the scalar Gaussian random field observation into one of several classes specified by different regression mean models and common parametric covariance function is considered. The classifier based on the plug-in Bayes classification rule formed by replacing unknown parameters in Bayes classification...
Article
Full-text available
This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) wi...
Article
The problem of classifying a scalar Gaussian random field observation into one of two populations specified by a different parametric drifts and common covariance model is considered. The unknown drift and scale parameters are estimated using given a spatial training sample. This paper concerns classification procedures associated to a parametric p...
Chapter
The problem of classifying a multivariate Gaussian random field (GRF) single observation into one of two populations specified by different parametric mean models and common intrinsic covariogram is considered. This paper concerns with classification procedures associated with the linear Bayes Discriminant Function (BDF) under the deterministic spa...
Article
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This paper describes the concept of teaching methods and their distribution to the various scientific methods, in addition to described innovative teaching methods. Considered innovative teaching methods advantages and disadvantages of teaching mathematics, the math topics examples. College mathematics teachers survey showed that the most applied a...
Article
In this paper we use the pluged-in Bayes discriminant function (PBDF) for classification of spatial Gaussian data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. The pluged-in Bayes discriminant function is constructed by using ML estimators of unknown mean and anisotro...
Article
Full-text available
For given training sample, the problem of supervised classifying the multivariate stationary Gaussian random field (GRF) observations into one of two populations is considered. The populations are specified by different regression mean models and by common factorized covariance function. For completely specified populations the formula of Bayes err...
Article
Full-text available
The paper deals with a problem of classification of Gaussian spatial data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. In the case of an unknown mean and covariance parameters the Plug-in Bayes discriminant function based on ML estimators is used. The asymptotic appr...
Article
Given training sample, the problem of classifying a scalar Gaussian random field observation into one of two populations specified by different parametric mean models and common parametric covariance function is considered. Such problems are usually called as supervised classification or contextual classification problems. This paper concerns with...
Article
Full-text available
Paper deals with a problem of testing isotropy against geometric anisotropy for Gaussian spatial data. The original simple test statistic based on directional empirical semivariograms is proposed. Under the assumption of independence of the classical semivar-iogram estimators and for increasing domain asymptotics, the distribution of test statistic...

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