The Journal of Polish Biometric Society

Biometrical Letters

Biometrical LettersONL

<abstract><title style='display:none'>Summary</title> The Birnbaum-Saunders (BS) model is a life distribution that has been widely studied and applied. Recently, a new version of the BS distribution based on extreme value theory has been introduced, named the extreme value Birnbaum-Saunders (EVBS) distribution. In this article we provide some further details on the EVBS models that can be useful as a supplement to the existing results. We use these models to analyse real survival time data for patients treated with alkylating agents for multiple myeloma. This analysis allow us to show the adequacy of these new statistical distributions and identify them as models useful for medical practitioners in order to predict survival times for such patients and evaluate changes in their treatment dose.</abstract>

Summary

Universidade de Lisboa, DEIO, CEAUL and FCUL, Portugal

Departamento de Matemática, Universidade do Minho, Portugal

Departamento de Estadística, Universidad de Valparaíso, Chile

Keywords

<article-title>The extreme value Birnbaum-Saunders model, its moments and an application in biometry</article-title>

The extreme value Birnbaum-Saunders model, its moments and an application in biometry

<abstract><title style='display:none'>Summary</title> Abnormal patterns of the retinal vascular system have been associated with several heart and cerebral diseases. However, these correlations were obtained using 2D imaging of the eye fundus, disregarding information on the third component (depth) of the vascular system. In this paper, we show that the depth component is relevant and should not be disregarded in this analysis. In this way, the relevance of analysis of the retinal vascular network as a biometrical marker for the general health status of an individual can be increased.</abstract>

Department of Science and Technology, Portuguese Open University, Portugal

Institute of Biomedical Research in Light and Image, Faculty of Medicine, University of Coimbra, Coimbra, Portugal

CNTM, Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal

<article-title>On the relevance of the 3D retinal vascular network from OCT data</article-title>

On the relevance of the 3D retinal vascular network from OCT data

{"article-title":"On the relevance of the 3D retinal vascular network from OCT data"}

<abstract><title style='display:none'>Summary</title> To model cross-sectional growth data the LMS method is widely applied. In this method the distribution is summarized by three parameters: the Box-Cox power that converts outcome to normality (L); the median (M); and the coeficient of variation (S).Here, we propose an alternative approach based on fitting finite mixture models with several components which may perform better than the LMS method in case the data show an unusual distribution. Further, we explore fixing the weights of the mixture components in contrast to the standard approach where weights are estimated. Having fixed weights improves the speed of computation and the stability of the solution. In addition, fixing the weights provides almost as good a fit as when the weights are estimated. Our methodology combines Gaussian mixture modelling and spline smoothing. The estimation of the parameters is based on the joint modelling of mean and dispersion.We illustrate the methodology on the Fourth Dutch Growth Study, which is a cross-sectional study that contains information on the growth of 7303 boys as a function of age. This information is used to construct centile curves, so-called growth curves, which describe the distribution of height as a smooth function of age. Further, we analyse simulated data showing a bimodal structure at some time point.In its full generality, this approach permits the replacement of the Gaussian components by any parametric density. Further, different components of the mixture can have a diferent probabilistic (multivariate) structure, allowing for censoring and truncation. </abstract>

Department of Biostatistics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, Netherlands

Catholic University of Leuven, L-Biostat, U.Z. St. Rafael, Kapucijnenvoer 35, 3000 Leuven, Belgium

<article-title>Finite mixture models with fixed weights applied to growth data</article-title>

Finite mixture models with fixed weights applied to growth data

{"article-title":"Finite mixture models with fixed weights applied to growth data"}

<abstract><title style='display:none'>Summary</title> One of the most important characteristics in determining the dendrometrical properties of a stand is the annual height increment of the trees. On the basis of these increments, natural phases of the trees’ life are defined. In the present study the data concern the height increments of the main trunk of 25-year-old Scots pine, as well as simulated data. Our research deals with the application of longitudinal data analysis. This analysis is usually used when measurements are taken at the same treatments at different time points. The calculations performed provide answers to the question of which annual height increments differ.</abstract>

Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland

Keywords 

<article-title>Application of longitudinal analysis to the analysis of height increment of pine stands – simulated data</article-title>

Application of longitudinal analysis to the analysis of height increment of pine stands – simulated data

<abstract><title style='display:none'>Summary</title> The discovery of knowledge in the case of Hierarchical Cluster Analysis (HCA) depends on many factors, such as the clustering algorithms applied and the strategies developed in the initial stage of Cluster Analysis. We present a global approach for evaluating the quality of clustering results and making a comparison among different clustering algorithms using the relevant information available (e.g. the stability, isolation and homogeneity of the clusters). In addition, we present a visual method to facilitate evaluation of the quality of the partitions, allowing identification of the similarities and differences between partitions, as well as the behaviour of the elements in the partitions. We illustrate our approach using a complex and heterogeneous dataset (real horse data) taken from the literature. We apply HCA based on the generalized affinity coefficient (similarity coefficient) to the case of complex data (symbolic data), combined with 26 (classic and probabilistic) clustering algorithms. Finally, we discuss the obtained results and the contribution of this approach to gaining better knowledge of the structure of data.</abstract>

University of Azores, Department of Mathematics, CMATI, 9501-855-Ponta Delgada, Portugal

University of Lisbon, Faculty of Psychology, Laboratory of Statistics and Data Analysis 1649-013-Lisboa, Portugal, and DataScience

New University of Lisbon, FCT, Department of Mathematics, 2829-516-Caparica, Portugal, and DataScience

<article-title>A Global Approach to the Comparison of Clustering Results</article-title>

A Global Approach to the Comparison of Clustering Results

{"article-title":"A Global Approach to the Comparison of Clustering Results"}

<abstract><title style='display:none'>Summary</title> This paper demonstrates the possible conclusions which can be drawn from an analysis of entropy and information. Because of its universality, entropy can be widely used in different subjects, especially in biomedicine. Based on simulated data the similarities and differences between the grouping of attributes and testing of their independencies are shown. It follows that a complete exploration of data sets requires both of these elements. A new concept introduced in this paper is that of normed information gain, allowing the use of any logarithm in the definition of entropy.</abstract>

Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Poznań, Poland

<article-title>The use of information and information gain in the analysis of attribute dependencies</article-title>

The use of information and information gain in the analysis of attribute dependencies

<abstract><title style='display:none'>Summary</title> The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.</abstract>

Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Akademicka 13, 20-934 Lublin, Poland

<article-title>On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality</article-title>

On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality

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Biometrical Letters ist in den folgenden Services indiziert: <UL> <LI>Arianta </LI> <LI>Baidu Scholar </LI> <LI>CABI (over 50 subsections) </LI> <LI>Case </LI> <LI>CNKI Scholar (China National Knowledge Infrastructure) </LI> <LI>CNPIEC - cnpLINKer </LI> <LI>Dimensions </LI> <LI>EBSCO </LI> <LI>ExLibris </LI> <LI>Genamics JournalSeek </LI> <LI>Google Scholar </LI> <LI>Index Copernicus </LI> <LI>Japan Science and Technology Agency (JST) </LI> <LI>J-Gate </LI> <LI>JournalTOCs </LI> <LI>KESLI-NDSL (Korean National Discovery for Science Leaders) </LI> <LI>Meta </LI> <LI>MyScienceWork </LI> <LI>Naver Academic </LI> <LI>Naviga (Softweco) </LI> <LI>Polish Scientific Journals Contents </LI> <LI>Publons </LI> <LI>QOAM (Quality Open Access Market) </LI> <LI>ReadCube </LI> <LI>Semantic Scholar </LI> <LI>Sherpa/RoMEO </LI> <LI>TDNet </LI> <LI>Ulrich's Periodicals Directory/ulrichsweb </LI> <LI>WanFang Data </LI> <LI>WorldCat (OCLC) </LI> <LI>X-MOL </LI></UL>

Editor-in-Chief Stanisław Mejza, Poznan University of Life Sciences ul. Wojska Polskiego 28, PL-60-637 Poznań, Poland e-mail: <A href="mailto:smejza@up.poznan.pl">smejza@up.poznan.pl</A> Maria Kozłowska, Poznan University of Life Sciences ul. Wojska Polskiego 28, PL-60-637 Poznań, Poland e-mail: <A href="mailto:markoz@up.poznan.pl">markoz@up.poznan.pl</A> Co-Editors Jerzy Moczko, Poznan University of Medical Sciences ul. Dąbrowskiego 79, PL-60-529 Poznań, Poland e-mail: <A href="mailto:jmoczko@amp.edu.pl">jmoczko@amp.edu.pl</A> Wiesław Mądry, Warsaw University of Life Sciences ul. Nowoursynowska 159, PL-02-776 Warszawa, Poland e-mail: <A href="mailto:wieslaw.madry@omega.sggw.waw.pl">wieslaw.madry@omega.sggw.waw.pl</A> Paulo Canas Rodrigues, Federal University of Bahia, Av. Adhemar de Barros, 40.170-110 Salvador, BA, Brazil, email: <A href="mailto:paulocanas@gmail.com">paulocanas@gmail.com</A> Editorial Board Are Aastveit (Norwegian University of Life Sciences, Aas, Norway), Anthony C. Atkinson (London School of Economics, UK), Rosemary A. Bailey (University of St Andrews, Scotland) Tadeusz Caliński (Poznań University of Life Sciences, Poznań, Poland), Janusz Gołaszewski (University of Warmia and Mazury, Olsztyn, Poland), Zofia Hanusz (University of Life Sciences, Lublin, Poland), Chihiro Hirotsu (Meisei University, Tokyo, Japan), Christos Kitsos (Technological Educational Institute of Athens, Greece), Kristian M. Kristensen (Aarhus University, Aarhus, Denmark), Shinji Kuriki (Osaka Prefecture University, Osaka, Japan), Victor Leiva (Pontificia Universidad Católica de Valparaíso, Chile), Tadeusz Łuczkiewicz (Poznań University of Life Sciences, Poznań, Poland), Iwona Mejza (Poznań University of Life Sciences, Poznań, Poland), Joao T. Mexia (New University of Lisboa, Portugal), Amilcar Oliveira (Universidade Aberta, Lisboa, Portugal), Teresa A. Oliveira (Universidade Aberta, Lisboa, Portugal), Dulce G. Pereira (University of Evora, Evora, Portugal), Christel Richter (Humboldt-Universitat zu Berlin, Germany), Zbigniew Sobek (Poznań University of Life Sciences, Poznań, Poland), Milan Stehlik (Johannes Kepler University Linz, Austria). Language Editors John Catlow, Adam Mickiewicz University, Poznań, Poland /UK Publisher De Gruyter Poland Bogumiła Zuga 32A Str. 01-811 Warsaw, Poland T: +48 22 701 50 15

<DIV align=left> Please submit your manuscripts to Biometrical Letters via email to: <A href="mailto:biometrical.letters@up.poznan.pl">biometrical.letters@up.poznan.pl</A> Open Access Statement The journal is an Open Access journal that allows a free unlimited access to all its contents without any restrictions upon publication to all users. <A href="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/BILE/Open_Access_License.pdf">Open Access License</A> </DIV>

<DIV align=left> Biometrical Letters is the official journal of the Polish Biometric Society, founded in 1964, now published with the financial support of the Ministry of Science and Higher Education. The anonymous double-blind peer review by the two independent reviewers is applied to all the papers. The purposes of Biometrical Letters are: <UL> <LI>publishing papers dealing with various aspects of the analysis and interpretation of results from biometrical experiments, using mathematical and statistical methods; </LI> <LI>promoting and extending the use of statistical and mathematical methods in the principal disciplines of life sciences by reporting on the development and application of these methods; </LI> <LI>presenting statistical and mathematical tools useful in biometry. Methodological developments should be motivated by interesting problems from life sciences, applied or applicable; </LI> <LI>publishing review papers and papers presenting practical use of various methods of data analysis and statistical inference. </LI></UL> The core of most Biometrical Letters articles is statistical inference that sets scientific or policy objectives, motivates methodological development and demonstrates the operation of new methods. The paper should include a description of the biometrical problem and a section detailing the application of the new methodology. The Journal welcomes the submission of manuscripts from bioscience, mathematical and statistical theory, biology, genetics, medical statistics, agriculture, forestry, ecology, environmental sciences, biometrics and clinical trials. Case studies and articles on the use and development of statistical software are also welcome. Rejection Rate <UL> <LI>20% </LI></UL> Archiving Sciendo archives the contents of this journal in <A href="https://www.portico.org/">Portico</A> - digital long-term preservation service of scholarly books, journals and collections. Plagiarism Policy The editorial board is participating in a growing community of <A href="https://www.crossref.org/services/similarity-check/">Similarity Check System's</A> users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. </DIV>

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Biometrical Letters

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Volume 49 (2012): Edition 2 (December 2012)

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