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Influence diagnostics for the structural sharpe model under normal/independent distributions

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2019年第8期48卷 1815-1835页

作者： Zeller, Camila Borelli Vilca, Filidor Galea, Manuel Univ Fed Juiz de Fora Dept Estat Juiz De Fora MG Brazil Univ Estadual Campinas Dept Estat Sao Paulo Brazil Pontificia Univ Catolica Chile Dept Estadist Santiago Chile

A method is proposed in this paper to assess the local influence of minor perturbations for the Sharpe model when the normal distribution is replaced by normal/independent (NI) distributions. The family of NI distributions is an attractive class of symmetric heavy-tailed densities that includes as special cases the normal, t-Student, slash, and the contaminated normal distributions. Since the returns of the market are not observable, the statistical analysis is carried out in the context of an errors-in-variables model. An influence analysis for detecting influential observations (atypical returns) is developed to investigate the sensitivity of the maximum likelihood estimators. Diagnostic measures are obtained based on the conditional expectation of the complete-data log-likelihood function. The results are illustrated by using a set of shares of companies traded in the Chilean stock market.

关键词： Diagnostics em-algorithm Errors-in-variables models Normal independent (NI) distributions Portfolios Sharpe's model

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Semiparametric generalized exponential frailty model for clustered survival data

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHemATICS 2019年第3期71卷 679-701页

作者： Barreto-Souza, Wagner Mayrink, Vinicius Diniz Univ Fed Minas Gerais Dept Estat Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

In this paper, we propose a novel and mathematically tractable frailty model for clustered survival data by assuming a generalized exponential (GE) distribution for the latent frailty effect. Both parametric and semiparametric versions of the GE frailty model are studied with main focus for the semiparametric case, where an em-algorithm is proposed. Our em-based estimation for the GE frailty model is simpler, faster and immune to a flat likelihood issue affecting, for example, the semiparametric gamma model, as illustrated in this paper through simulated and real data. We also show that the GE model is at least competitive with respect to the gamma frailty model under misspecification. A broad analysis is developed, with simulation results explored via Monte Carlo replications, to evaluate and compare models. A real application using a clustered kidney catheter data is considered to demonstrate the potential for practice of the GE frailty model.

关键词： Censored data em-algorithm Flat likelihood Gamma frailty model Partial likelihood Proportional hazards

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Mixture of Truthful-Untruthful Responses in Public Surveys

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INTERNATIONAL STATISTICAL REVIEW 2019年第3期87卷 557-579页

作者： Christofides, Tasos C. Perri, Pier Francesco Univ Cyprus Dept Math & Stat CY-1678 Nicosia Cyprus Univ Calabria Dept Econ Stat & Finance I-87036 Arcavacata Di Rende CS Italy

When sensitive issues are surveyed, collecting truthful data and obtaining reliable estimates of population parameters is a persistent problem in many fields of applied research mostly in sociological, economic, demographic, ecological and medical studies. In this context, and moving from the so-called negative survey, we consider the problem of estimating the proportion of population units belonging to the categories of a sensitive variable when collected data are affected by measurement errors produced by untruthful responses. An extension of the negative survey approach is proposed herein in order to allow respondents to release a true response. The proposal rests on modelling the released data with a mixture of truthful and untruthful responses that allows researchers to obtain an estimate of the proportions as well as the probability of receiving the true response by implementing the em-algorithm. We describe the estimation procedure and carry out a simulation study to assess the performance of the em estimates vis-a-vis certain benchmark values and the estimates obtained under the traditional data-collection approach based on direct questioning that ignores the presence of misreporting due to untruthful responding. Simulation findings provide evidence on the accuracy of the estimates and permit us to appreciate the improvements that our approach can produce in public surveys, particularly in election opinion polls, when the hidden vote problem is present.

关键词： direct questioning election opinion polls em-algorithm indirect questioning privacy protection untruthful reporting

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Construction of a semi-Markov model for the performance of a football team in the presence of missing data

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JOURNAL OF APPLIED STATISTICS 2019年第3期46卷 559-576页

作者： Danisman, Ozgur Kocer, Umay Uzunoglu Dokuz Eylul Univ Dept Stat Fac Sci Izmir Turkey

Using play-by-play data from the very beginning of the professional football league in Turkey, a semi-Markov model is presented for describing the performance of football teams. The official match results of the selected teams during 55 football seasons are used and winning, drawing and losing are considered as Markov states. The semi-Markov model is constructed with transition rates inferred from the official match results. The duration between the last match of a season and the very first match of the following season is much longer than any other duration during the season. Therefore these values are considered as missing values and estimated by using expectation-maximization algorithm. The effect of the sojourn time in a state to the performance of a team is discussed as well as mean sojourn times after losing/winning are estimated. The limiting probabilities of winning, drawing and losing are calculated. Some insights about the performance of the selected teams are presented.

关键词： em-algorithm estimation football log-normal distribution semi-Markov process sojourn time

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FLEXIBLE AND ROBUST MIXED POISSON INGARCH MODELS

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JOURNAL OF TIME SERIES ANALYSIS 2019年第5期40卷 788-814页

作者： Silva, Rodrigo B. Barreto-Souza, Wagner Univ Fed Minas Gerais Dept Estat Av Antonio Carlos BR-31270901 Belo Horizonte MG Brazil Univ Fed Paraiba Dept Estat Joao Pessoa Paraiba Brazil

In this article, we propose a general class of INteger-valued Generalized AutoRegressive Conditional Heteroskedastic (INGARCH) models based on a flexible family of mixed Poisson (MP) distributions. Our proposed class of count time series models contains the negative binomial (NB) INGARCH process as particular case and open the possibility to introduce new models such as the Poisson-inverse Gaussian (PIG) and Poisson generalized hyperbolic secant processes. In particular, the PIG INGARCH model is an interesting and robust alternative to the NB model. We explore first-order and second-order stationary properties of our MPINGARCH models and provide expressions for the autocorrelation function and mean and variance marginals. Conditions to ensure strict stationarity and ergodicity properties for our class of INGARCH models are established. We propose an Expectation-Maximization algorithm to estimate the parameters and obtain the associated information matrix. Further, we discuss two additional estimation methods. Monte Carlo simulation studies are considered to evaluate the finite-sample performance of the proposed estimators. We illustrate the flexibility and robustness of the MPINGARCH models through two real-data applications about number of cases of Escherichia coli and Campylobacter infections. This article contains a Supporting Information.

关键词： Count time series em-algorithm mixed Poisson distributions overdispersion stationarity

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Skew-Normal-Cauchy Linear Mixed Models

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SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS 2019年第2期81卷 185-202页

作者： Kahrari, F. Ferreira, C. S. Arellano-Valle, R. B. Shahid Chamran Univ Ahvaz Dept Stat Ahvaz Iran Univ Fed Juiz de Fora Dept Stat Juiz De Fora MG Brazil Pontificia Univ Catolica Chile Dept Stat Santiago Chile

In this work, a flexible class of linear mixed models is introduced by assuming that the random effects and model errors follow a skew-normal-Cauchy distribution. The likelihood function and the information matrix based on of the observed data are computed. An em-type algorithm is also proposed for estimating the parameters that seems to provide some advantages over a direct maximization of the likelihood function. Finally, the performance of the proposed model is evaluated numerically from simulated an real data.

关键词： em-algorithm Shape mixture Skew-normal-cauchy distribution Mixed effects model

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Separation of a Mixture of Three-Parameter Lognormal Distributions in the Analysis of Communication Environments

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Computational Mathematics and Modeling 2019年第3期30卷 311-319页

作者： Kozlov, V.D. Maysuradze, A.I. Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russian Federation

Methods for the separation of a mixture of three-parameter lognormal distributions are investigated theoretically and empirically in the context of modeling message transmission delays in a computer cluster communication environment. Delay modeling based on mixtures of three-parameter lognormal distributions is proposed and parameter estimation methods for such models are investigated. Identifiability of the given family of distributions is proved, which is a necessary condition for the mixture separation problem to be well-posed. The proposed problem-oriented modifications of the standard algorithms are shown to be superior to the traditional methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： computer cluster communication environment em-algorithm identifiability of a family of distributions message transmission delays point estimation problem-oriented methods separation of a mixture of distributions three-parameter lognormal distribution

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Modeling and Self-Similarity Analysis of Non-Poissonian Traffic Represented by Multimodal Non-Typical Pascal and Rice Distributions

Modeling and Self-Similarity Analysis of Non-Poissonian Traf...

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Systems of Signals Generating and Processing in the Field of on Board Communications Conference

作者： Faizullin, R. R. Yaushev, S. T. Insarov, A. Y. Kazan Natl Res Tech Univ Kazan Russia

ISBN: (纸本)9781728106069

Modern stage of development of information and communication networks requires solving of crucial tasks of network traffic statistical analysis and traffic simulation modeling. The predominance of non-Poisson traffic leads to the impossibility of analyzing multichannel communication systems by the methods of queuing theory that used to describe telephone networks. The last decade has paid much attention to research on traffic that has signs of self-similarity. The main purpose of this work is a statistical analysis of non-Poissonian traffic, represented by multimodal non-standard Pascal (negative binomial) and Rice distributions. As a result, a study of the self-similarity degree has been performed by the R/S analysis and the aggregation method. In addition, we propose to use em-algorithm with an algorithm for determining an optimal number of clusters for an approximation of non-typical multimodal distributions.

关键词： self-similar traffic Rice distribution Pascal distribution em-algorithm QoS

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Online Hybrid Probabilistic-Fuzzy Clustering in Medical Data Mining Tasks 2

Online Hybrid Probabilistic-Fuzzy Clustering in Medical Data...

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2nd International Workshop on Informatics and Data-Driven Medicine (IDDM)

作者： Bodyanskiy, Yevgeniy Deineko, Anastasiia Pliss, Iryna Chala, Olha Kharkiv Natl Univ Radio Elect Control Syst Res Lab Kharkiv Ukraine

In the paper the online fuzzy clustering recurrent procedure has been introduced that allows the forming of hyperellipsoidal clusters with an arbitrary orientation of the axes is investigated. The proposed clustering system is the generalization of a number of known algorithms, it is intended to solve tasks within the general problems of Medical Data Mining, when information is sequentially fed to processing in online mode.

关键词： Medical Data Mining Big Data Computational Intelligence Fuzzy Clustering em-algorithm Kohonen's Self-Learning Soft Clustering

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Machine Learning Approach to the Synthesis of Identification Procedures for Modern Photon-Counting Sensors 8th

Machine Learning Approach to the Synthesis of Identification...

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8th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Antsiperov, Viacheslav Russian Acad Sci Kotelnikov Inst Radioengn & Elect Mokhovaya 11-7 Moscow Russia

ISBN: (纸本)9789897583513

The article presents the results of developing a machine learning approach to the problem of object identification (recognition) in images (data) recorded by photo-counting sensors. Such images are significantly different from the traditional ones, taken with conventional sensors in the process of time exposure and spatial averaging of the incident radiation. The result of radiation registration by photo-counting sensors (image) is rather a continuous stream of data, whose time frame is characterized by a relatively small number of photocounts. The latter leads to a low signal-to-noise ratio, low contrast and fuzzy shapes of the objects. For this reason, the well-known methods, designed for traditional image recognition, are not effective enough in this case and new recognition approaches, oriented to a low-count images, are required. In this paper we propose such an approach. It is based on the machine learning paradigm and designed for identifying (low count) objects given by point-sets. Consistently using a discrete set of coordinates of photocounts rather than a continuous image reconstructed, we formalize the problem in question as the problem of the best fitting of this set of counts, considered as the realization of a certain point process, to the statistical description of one of the previously registered point processes, which we call precedents. It is shown, that applying the Poisson point process model for formalizing the registration process in photo-counting sensors, it is possible to reduce the problem of object identification to the problem of maximizing the tested point--set likelihood with respect to the classes of modelling object distributions up to shape size and position. It is also demonstrated that these procedures can be brought to an algorithmic realization, analogous in structure to the popular em algorithms. At the end of the paper we, for the sake of illustration, present some results of applying the developed algorithms to the identifica

关键词： Photon-Counting Sensors Target Detection and Identification Semiclassical Theory of Light Detection Poisson Point Process Machine Learning em-algorithm

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