<abstract xmlns="http://www.w3.org/1999/xhtml"> Step change-point and slope change-point models in the independent Poisson sequence are developed based on accumulated and doubly-accumulated statistics. The method for the step change-point model developed in Section 2 is an alternative to the likelihood ratio test of Worsley (1986) and the algorithm for p-value calculation based on the first-order Markov property is the same as that given there. Different algorithms for the non-null distribution and inference on the change-point itself are, however, newly developed and a Pascal program is given in the Appendix. These methods are extended to the slope change-point model in Section 3. The approach is essentially the same as that of Section 2 but the algorithm is now based on the second-order Markov property and becomes a little more complicated. The Pascal program related to the slope change-point model is supported on the website, URL: https://corec.meisei-u.ac.jp/labs/hirotsu/.</abstract>

An algorithm for a new method of change-point analysis in the independent Poisson sequence

<abstract xmlns="http://www.w3.org/1999/xhtml"> In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed.</abstract>

A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes

<abstract xmlns="http://www.w3.org/1999/xhtml"> In studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.</abstract>

Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

<abstract xmlns="http://www.w3.org/1999/xhtml"> The aim of the study was to evaluate the effect of mineral fertilization and seed quality on the yield of winter wheat in production conditions. This assessment is made in terms of the expected probabilities of success in relation to the yield of analyzed cultivars, taking into account the interaction of the factors considered. Analyses were performed on data from 3815 fields. The impact of fertilizers and seed quality was evaluated using logistic regression. Grain yield was transformed into a binomial variable, where values were divided into two classes, i.e. below the mean and above the mean. The results of the analysis proved a significant effect of fertilization rate, which was modified by different seed quality. The highest probability of obtaining yields above the mean was observed for pre-basic and basic seed quality at high fertilizer rates.</abstract>

Evaluation of the impact of fertilizers and seed quality on winter wheat yield

<abstract xmlns="http://www.w3.org/1999/xhtml"> The differences between individual breeding lines of spring barley and a control variety were tested in terms of several measured (continuous) and qualitative traits. The impact of the qualitative traits (diseases) on the quantitative traits, especially yield, was assessed on the basis of the significance of differences for both qualitative traits and yield. Depending on the type of trait, either a logistic model or analysis of variance was used as a statistical tool. The statistically significant differences between some breeding lines and the control variety were shown. It was observed that in fodder barley both infection by mildew and lodging influenced yield. The results of analyses obtained in the so-called pre-preliminary trials and preliminary trials were different. This fact confirmed the necessity of repeating trials over several years.</abstract>

Impact of cereal diseases on the qualitative traits of spring barley breeding lines

AHEAD OF PRINT

Volume 60 (2023): Issue 2 (December 2023)

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Volume 59 (2022): Issue 2 (December 2022)

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Volume 57 (2020): Issue 2 (December 2020)

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Volume 56 (2019): Issue 2 (December 2019)

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Volume 54 (2017): Issue 2 (December 2017)

Volume 54 (2017): Issue 1 (June 2017)

Volume 53 (2016): Issue 2 (December 2016)

Volume 53 (2016): Issue 1 (June 2016)

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Volume 51 (2014): Issue 2 (December 2014)

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Volume 50 (2013): Issue 2 (December 2013)

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

Volume 49 (2012): Issue 1 (June 2012)

Biometrical Letters

 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:   publishing papers dealing with various aspects of the analysis and interpretation of results from biometrical experiments, using mathematical and statistical methods;  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;  presenting statistical and mathematical tools useful in biometry. Methodological developments should be motivated by interesting problems from life sciences, applied or applicable;  publishing review papers and papers presenting practical use of various methods of data analysis and statistical inference.   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   20%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's 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. 