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Finite Mixtures of Mean-Parameterized Conway-Maxwell-Poisson Regressions

JOURNAL OF STATISTICAL THEORY AND PRACTICE 2024年第1期18卷 8-8页

作者： Zhan, Dongying Young, Derek S. Univ Kentucky Dr Bing Zhang Dept Stat 725 Rose St Lexington KY 40536 USA

As a generalization of the Poisson distribution and a common alternative to other discrete distributions, the Conway-Maxwell-Poisson (CMP) distribution has the flexibility to explicitly characterize data over- or under-dispersion. The mean-parameterized version of the CMP has received increasing attention in the literature due to its ability to directly model the data mean. When the mean further depends on covariates, then the mean-parameterized CMP regression model can be treated in a generalized linear models framework. In this work, we propose a mixture of mean-parameterized CMP regressions model to apply on data which are potentially comprised of subpopulations with different conditional means and varying degrees of dispersions. An EM algorithm is constructed to find maximum likelihood estimates of the model. A simulation study is performed to test the proposed mixture of mean-parameterized CMP regressions model, and to compare it to model fits using mixtures of Poisson regressions and mixtures of negative binomial regressions. We show the mixture of mean-parameterized CMP regressions to be a competitive model in analyzing two real datasets.

关键词： Bootstrap Data dispersion EM algorithm generalized linear models Negative binomial

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De-noising boosting methods for variable selection and estimation subject to error-prone variables

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STATISTICS AND COMPUTING 2023年第2期33卷 38-38页

作者： Chen, Li-Pang Natl Chengchi Univ Dept Stat Taipei Taiwan

Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of boosting is to sequentially apply the algorithm to enhance its performance. Recently, boosting methods have been implemented to handle variable selection. However, little work has been available to deal with complex data such as measurement error in covariates. In this paper, we adopt the boosting method to do variable selection, especially in the presence of measurement error. We develop two different approximated correction approaches to deal with different types of responses, and meanwhile, eliminate measurement error effects. In addition, the proposed algorithms are easy to implement and are able to derive precise estimators. Throughout numerical studies under various settings, the proposed method outperforms other competitive approaches.

关键词： Error correction generalized linear models Mismeasurement Regression calibration SIMEX Statistical learning

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generalized linear Factor models: A New Local EM Estimation Algorithm

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2013年第16期42卷 2944-2958页

作者： Saidane, Mohamed Bry, Xavier Lavergne, Christian Univ Carthage ISCCB Zarzouna 7021 Bizerte Tunisia Univ Montpellier 2 UMR CNRS I3M Montpellier France

In this article, a general approach to latent variable models based on an underlying generalized linear model (GLM) with factor analysis observation process is introduced. We call these models generalized linear Factor models (GLFM). The observations are produced from a general model framework that involves observed and latent variables that are assumed to be distributed in the exponential family. More specifically, we concentrate on situations where the observed variables are both discretely measured (e.g., binomial, Poisson) and continuously distributed (e.g., gamma). The common latent factors are assumed to be independent with a standard multivariate normal distribution. Practical details of training such models with a new local expectation-maximization (EM) algorithm, which can be considered as a generalized EM-type algorithm, are also discussed. In conjunction with an approximated version of the Fisher score algorithm (FSA), we show how to calculate maximum likelihood estimates of the model parameters, and to yield inferences about the unobservable path of the common factors. The methodology is illustrated by an extensive Monte Carlo simulation study and the results show promising performance.

关键词： EM algorithm Factor models generalized linear models Scores algorithm Simulations Primary 62H25 Secondary 62J12

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Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities

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SUSTAINABLE FUTURES 2024年 8卷

作者： Bonfiglio, Andrea Council Agr Res & Econ CREA Res Ctr Agr Pol & Bioecon Via Thomas Edison 2 I-60027 Osimo Ancona Italy

Innovation within a company, sector, or territorial context can arise from identifying specific problems or needs. Performance analysis provides valuable insights, potentially highlighting areas where innovation may be necessary. This paper outlines a methodology designed to identify latent innovation needs at regional, sectoral, and dimensional levels. This methodology is based on critical issues emerging from comparing economic, environmental, and social sustainability indicators of farms. An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. Logistic regression is adopted to calculate the propensity scores, and several generalized linear models are used to estimate the impact of regional localization. Hypothesis tests are then conducted to confirm the statistical significance of the impacts, from which criticality levels are derived. This methodology is applied to data collected by the Italian Farm Accountancy Data Network, which includes over 64,000 observations from two consecutive three-year periods (2016-2018 and 2019-2021). The results highlight the existence of widespread critical issues in Italian agriculture. These problems primarily concern the efficient use of water, whose costs, relative to revenue, exceed, on average, around 40 %, and greenhouse horticulture, which shows an increased criticality level in terms of sustainability of over 50 %. From a regional perspective, Puglia in Southern Italy exhibits the most evident critical issues, with an average criticality level of 30 % that has increased over time. The emerging criticalities are analyzed in relation to the interventions introduced by the 2023-2027 Common Agricultural Policy to verify their coherence and identify possible innovative actions that can be implemented.

关键词： Innovation needs FADN Mixed matching generalized linear models G -computation 2023-2027 CAP

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Smooth Spatial Modeling of Extreme Mediterranean Precipitation

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WATER 2022年第22期14卷 3782页

作者： Hammami, Hela Carreau, Julie Neppel, Luc Elasmi, Sadok Feki, Haifa Univ Montpellier CNRS HydroSci Montpellier IRD F-34093 Montpellier France Univ Carthage Higher Sch Commun Tunis COSIM Lab Ariana 2083 Tunisia Polytech Montreal Dept Math & Ind Engn Montreal PQ H3T 1J4 Canada Univ Carthage GREEN TEAM Lab LR17AGR01 Tunis 1082 Tunisia

Extreme precipitation events can lead to disastrous floods, which are the most significant natural hazards in the Mediterranean regions. Therefore, a proper characterization of these events is crucial. Extreme events defined as annual maxima can be modeled with the generalized extreme value (GEV) distribution. Owing to spatial heterogeneity, the distribution of extremes is non-stationary in space. To take non-stationarity into account, the parameters of the GEV distribution can be viewed as functions of covariates that convey spatial information. Such functions may be implemented as a generalized linear model (GLM) or with a more flexible non-parametric non-linear model such as an artificial neural network (ANN). In this work, we evaluate several statistical models that combine the GEV distribution with a GLM or with an ANN for a spatial interpolation of the GEV parameters. Key issues are the proper selection of the complexity level of the ANN (i.e., the number of hidden units) and the proper selection of spatial covariates. Three sites are included in our study: a region in the French Mediterranean, the Cap Bon area in northeast Tunisia, and the Merguellil catchment in central Tunisia. The comparative analysis aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.

关键词： intense precipitation events non-stationarity in space generalized extreme value distribution spatial interpolation generalized linear models artificial neural networks

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The Co-evolution of Organizational and Network Structure: The Role of Multilevel Mixing and Closure Mechanisms

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ORGANIZATIONAL RESEARCH METHODS 2021年第2期24卷 285-318页

作者： Amati, Viviana Lomi, Alessandro Mascia, Daniele Pallotti, Francesca Swiss Fed Inst Technol Social Networks Lab Dept Humanities Social & Polit Sci Zurich Switzerland Univ Italian Switzerland Inst Computat Sci Via Buffi 13 CH-6900 Lugano Switzerland Univ Exeter Business Sch Exeter Devon England LUISS Guido Carli Univ Dept Business & Management Rome Italy Univ Greenwich Dept Int Business & Econ Ctr Business Network Anal Old Royal Naval Coll Pk Row London England

We present a dynamic multilevel framework for analyzing the mutual dependence of change in interorganizational networks and internal organizational structure. Change occurring at the former (interorganizational) level involves decisions to change the portfolio of network ties to external partners. Change occurring in the latter (intraorganizational) level involves decisions to change the portfolio of internal activities. We estimate a recently derived class of stochastic actor-oriented models (SAOMs) that we adopt and adapt to specify how decisions to change internal portfolios of activities and external portfolios of partners are connected by theoretically derived multilevel mechanisms that link organizational and network structures. We show that statistical models for multilevel networks reproduce with high fidelity the structural regularities observed in the distribution of (a) activities within organizations, (b) network ties between organizations, and (c) knowledge available in the organizational field. We discuss the implications of the study for theory development, and for empirical research on interorganizational and other kinds of multilevel networks.

关键词： generalized linear models health care organizations interorganizational networks multilevel networks network evolution organizational change organizational fields social mechanisms stochastic actor-oriented models

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The Focused Information Criterion for Logistic Time Series Regression models Under Locally Biased Estimating Functions

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021年第2期15卷 1-23页

作者： Ramanathan, T. V. Pandhare, S. C. SP Pune Univ Dept Stat Pune Maharashtra India

Statistical modeling of biomedical data arising from the cytologic samples collected during cancer trials often involves the analysis of binary time series responses indicating the status of the samples being benign or malignant. The model selection problems pertaining to such data involve exploring the Markovian dependence of the status of the cytologic samples collected in a timely manner on the potential covariates of the malignancy measured during each phase of the trial. In the present paper, we propose the focused information criterion (FIC) that facilitates the simultaneous selection of the order of the binary autoregressive response and a subset of covariates in logistic time series regression models. The FIC attempts to select the subset of covariates that minimizes the mean squared error in estimating a certain focus function of interest under the potential model misspecification. The focus function is chosen so as to adequately represent the purpose of statistical modeling for the concerned data. We derive the general expressions for the FIC using quasimaximum likelihood estimators when the quasi-likelihood estimating functions are locally biased. In terms of minimizing the estimated mean squared error of the focus function, our FIC is seen to outperform the traditional FIC that ignores the bias in the underlying estimating functions induced by the potential model misspecification.

关键词： Biased estimating functions Focused modeling generalized linear models Local misspecification Model selection Logistic time series regression Quasi-likelihood estimation

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Multiple Systems Estimation for Modern Slavery: Robustness of List Omission and Combination

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CRIME & DELINQUENCY 2021年第13-14期67卷 2213-2236页

作者： Sharifi Far, Serveh King, Ruth Bird, Sheila Overstall, Antony Worthington, Hannah Jewell, Nicholas Univ Edinburgh Sch Math Stat Edinburgh Midlothian Scotland Univ Cambridge Cambridge Cambs England Univ Southampton Stat Southampton Stat Sci Res Inst Southampton Hants England Univ St Andrews Stat Sch Math & Stat St Andrews Fife Scotland London Sch Hyg & Trop Med Biostat & Epidemiol London England

Performing censuses on stigmatized or vulnerable populations is challenging, however, for such populations partial enumeration is often possible using different lists or sources. If the sources overlap then multiple systems estimation (MSE) methods can be applied to obtain an estimate of the total population. These are typically expressed by a log-linear model which permits positive/negative dependencies between lists. This paper considers issues that arise for the application of MSE to modern slavery where there is little to no overlap of individuals across lists. We investigate the robustness of MSE in terms of the importance of each list and the impact of combining lists on the estimation process. We undertake a simulation study and consider real national modern slavery data from the UK and Romania.

关键词： combining sources estimate stability generalized linear models list omission

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SAMPLE EFFICIENT SUBSPACE-BASED REPRESENTATIONS FOR NONlinear META-LEARNING

SAMPLE EFFICIENT SUBSPACE-BASED REPRESENTATIONS FOR NONLINEA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Gulluk, Halil Ibrahim Sun, Yue Oymak, Samet Fazel, Maryam Bogazici Univ Istanbul Turkey Univ Washington Seattle WA 98195 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781728176055

Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from common patterns of previously seen tasks so that a future task can be learned quickly. While recent works show the benefit of subspace-based representations, such results are limited to linear-regression tasks. This work explores a more general class of nonlinear tasks with applications ranging from binary classification, generalized linear models and neural nets. We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity. Numerical results verify the theoretical predictions in classification and neural-network regression tasks.

关键词： representation learning binary classification generalized linear models nonlinear problems

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Can anoxic atmospheres protect the quality of maize seeds during storage?

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JOURNAL OF STORED PRODUCTS RESEARCH 2022年 96卷 101927-101927页

作者： Abadia, M. B. San Martino, S. Bartosik, R. E. IPADS Balcarce INTA CONICET RN 226 Km 73-5 Balcarce Buenos Aires Argentina Univ Nacl Mar del Plata Fac Ciencias Agr NACT Estadist RN 226 Km 73-5 Balcarce Argentina

Low oxygen atmospheres have proven to be effective to protect seed quality in many species during storage. In maize, however, the evidence is ambiguous since positive, negative, and neutral effects were reported. These apparent contradictions might relate to the different experimental conditions explored in previous studies (i.e., combinations of oxygen concentration, relative humidity, and temperature). The aim of this work was to study the effect of anoxic atmospheres (< 1% oxygen) on germination and vigor of maize seeds stored under five combinations of relative humidity and temperature (65%, 75%, and 85% relative humidity at 25 & DEG;C, and 65% relative humidity at 30 & DEG;C and 35 & DEG;C) under strict control of experimental conditions. Data of germination and radicle emergence (vigor) were analyzed using generalized linear models. Our results show that the effect of anoxia on seed quality depends on relative humidity and temperature. At 25 & DEG;C, anoxia benefited both germination and vigor at 75% and 85% relative humidity compared to the normal atmosphere, while at 65% relative humidity the benefit was only detectable on vigor. Additionally, the benefit of anoxia on germination was higher at a relative humidity of 85% than at 75%. At 30 & DEG;C and 35 & DEG;C and 65% relative humidity, anoxia was neutral to seed germination and vigor. Anoxia, in conclusion, benefits seed quality under particular combinations of relative humidity and temperature usually found in real storage conditions. This turns anoxic storage in a potential technology to extend the shelf life of maize seeds for both small-farmers and seed companies.

关键词： Controlled atmospheres Hermetic storage Germination Vigor generalized linear models

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