Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data

The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the stochastic additive distortions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum likelihood estimators, as well as expressions for the increase in the mean square error of forecasting for the Bayes predictor are given. To compensate the misclassification effects, new consistent estimators and a new Bayes predictor, which take into account the distortion model, are constructed. The robustness of the developed techniques is demonstrated via computer simulations and a real-life case study.Peer Reviewe

Statistical analysis of high-order Markov dependencies

The paper deals with parsimonious models of integer valued time series. Such models are special cases of high-order Markov chain with a small number of parameters. Two new parsimonious models are presented. The first is Markov chain of order s with r partial connections, and the second model is called Markov chain of conditional order. Theoretical results on probabilistic properties and statistical inferences for these models are given

Global changes in extreme daily temperature since 1950

Copyright 2008 by the American Geophysical UnionExtreme value analysis of observed daily temperature anomalies from a new quasi-global data set indicates that extreme daily maximum and minimum temperatures (>98.5 or <1.5 percentile) have warmed for most regions since 1950. Changes in extreme anomalous daily temperatures are determined by fitting extreme value distributions with time-varying parameters. Changes in the distribution of anomaly exceedances above a high threshold are found to be statistically significant at the 10% level for most land areas when compared with a time-invariant distribution and with the unforced natural variability produced by a coupled climate model. The largest positive trends in the location parameter of the extreme distribution are found in Canada and Eurasia where daily maximum temperatures have typically warmed by 1 to 3 degrees C since 1950. The total area exhibiting positive trends is significantly greater than can be attributed to unforced natural variability. For most regions, positive trend magnitudes are larger and cover a greater area for daily minimum temperatures than for maximum temperatures. The comparatively small areas of cooling are found to be consistent with unforced natural climate variability. The North Atlantic Oscillation (NAO) is found to have a significant influence on extreme winter daily temperatures for many areas, with a negative NAO of one standard deviation reducing expected extreme winter daily temperatures by similar to 2 degrees C over Eurasia but increasing temperatures over northeastern North America

Сomparative study of arc-to-glow transition efficiency when use multilayer condensed materials in electric contacts

The paper presents and discusses results of the study of arc to glow transformation at breaking of DC inductive load of a low power (less than 10 J) and low voltage (about 250 V). The ratio in duration of arcing and glowing is investigated in dependence on circuit parameters, gas quenching medium and its pressure and in particular on contact material. The transition, to complete the study, was analyzed also by means of fast photography and radiation spectra measurements. On the basis of the results the conclusions on possibility of control of the arc to glow transformation, for practical use in low power contact switching devices, are formulated.Представлено і обговорено результати дослідження дуги, що горить, при відключенні постійного струму з індуктивним навантаженням малої потужності (менше 10 Дж) і низької напруги (біля 250 В). Відношення тривалості горіння дуги і жевріючого розряду досліджується в залежності від параметрів ланцюга, виду гасильного газу та його тиску і, головним чином, від контактного матеріалу. Для повноти дослідження змінення типу розряду аналізували також за допомогою швидкісної зйомки і вимірювання спектрів випромінювання. На основі одержаних результатів сформульовано висновки про можливість практичного застосування контролю переходу дуги в жевріючий розряд в малопотужних контактах комутуючих пристроїв.Представлены и обсуждены результаты исследования дуги, горящей при отключении постоянного тока с индуктивной нагрузкой малой мощности (менее 10 Дж) и низкого напряжения (около 250 В). Отношение продолжительности горения дуги и тлеющего разряда исследовано в зависимости от параметров цепи, вида гасящего газа и его давления и, главным образом, от контактного материала. Для полноты исследования смену типа разряда анализировали также с помощью скоростной съемки и измерения спектров излучения. На основании полученных результатов сформулированы выводы о возможности практического использования контроля перехода дуги в тлеющий разряд в маломощных контактах коммутирующих устройств

Нейросетевые модели биномиальных временных рядов в задачах анализа данных

This article is devoted to constructing neural network-based models for discrete-valued time series and their use in computer data analysis. A new family of binomial time series based on neural networks is presented, which makes it possible to approximate the arbitrary-type stochastic dependence in time series. Ergodicity conditions and an equivalence relation for these models are determined. Consistent statistical estimators for model parameters and algorithms for computer data analysis (including forecasting and pattern recognition) are developed.В данном сообщении рассматриваются задачи построения нейросетевых моделей дискретных временных рядов и использования их для компьютерного анализа данных. Представлено новое семейство нейросетевых моделей дискретных временных рядов, позволяющих аппроксимировать любой тип стохастической зависимости состояний временного ряда от его предыстории. Установлены условия эргодичности и отношение эквивалентности для этих моделей. Построены состоятельные статистические оценки параметров моделей и алгоритмы компьютерного анализа данных с использованием нейросетевых моделей: алгоритмы оценивания параметров, прогнозирования и распознавания образов

Statistical Diagnostics of Metastatic Involvement of Regional Lymph Nodes

The method of statistical classification with indicating patients that require more detailed diagnostics is proposed and analysed

Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data

The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the stochastic additive distortions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators and maximum likelihood estimators, as well as expressions for the increase in the mean square error of forecasting for the Bayes predictor are given. To compensate the misclassification effects, new consistent estimators and a new Bayes predictor, which take into account the distortion model, are constructed. The robustness of the developed techniques is demonstrated via computer simulations and a real-life case study

АСИМПТОТИЧЕСКИЙ АНАЛИЗ ОЦЕНОК МАКСИМАЛЬНОГО ПРАВДОПОДОБИЯ ПАРАМЕТРОВ БИНОМИАЛЬНОЙ УСЛОВНО АВТОРЕГРЕССИОННОЙ МОДЕЛИ ПРОСТРАНСТВЕННО-ВРЕМЕННЫХ ДАННЫХ

Asymptotic properties of the maximum likelihood estimators of parameters for a binomial conditionally autoregressive model of spatio-temporal data are studied. The asymptotic normality is proved and the asymptotic covariance matrix is found for the estimators. The results of computer experiments are presented.Исследованы асимптотические свойства оценок максимального правдоподобия параметров биномиальной условно авторегрессионной модели пространственно-временных данных. Доказана асимптотическая нормальность и найдена асимптотическая ковариационная матрица построенных оценок. Представлены результаты компьютерных экспериментов