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作者： Saram, Thilini PennState University Libraries

学位级别：Master of Science

Discrete choice models predict the choices among two or more discrete alternatives. We discuss some existing models but focus on the Multinomial Choice Model (MNL) and explain Expectation-Maximization (em) algorithms. We provide evidence that failing to account for product availability leads to bias in demand estimates and use an illustrative example to demonstrate this. We propose a new model accounting for product availability. To accomplish this, we use em algorithms and direct optimization of observed data log-likelihood for estimating maximum likelihood estimates by introducing product availability as a missing variable. We use a simulation study to compare the models' prediction accuracy and fit the new model to the illustrative example.

关键词： Discrete choice models em algorithms Missing variables

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RATING TRANSITIONS FORECASTING: A FILTERING APPROACH

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International Journal of Theoretical and Applied Finance 2023年第2-3期26卷 2350009-2350009页

作者： Cousin, Areski Lelong, Jérǒme Picard, Tom Nexialog Consulting 110 Av. de la République Paris 75011 France IRMA UMR 7501 Université de Strasbourg 7 rue René-Descartes 67084 Cedex France Univ. Grenoble Alpes CNRS Grenoble INP LJK Grenoble 38000 France

Analyzing the effect of business cycle on rating transitions has been a subject of great interest these last 15 years, particularly due to the increasing pressure coming from regulators for stress testing. In this paper, we consider that the dynamics of rating migrations, in a pool of credit references, is governed by a common unobserved latent Markov chain. We explain how the current state of the hidden factor, can be efficiently inferred from observations of rating histories. We then adapt the classical Baum-Welch algorithm to our setting and show how to estimate the latent factor parameters. Once calibrated, we may reveal and detect economic changes affecting the dynamics of rating migration, in real time. The filtering formula is then used to predict future transition probabilities according to the economic cycle without using any external covariates. We propose two filtering frameworks: a discrete and a continuous version. We demonstrate and compare the efficiency of both approaches on fictive data and on a corporate credit rating database. The methods could also be applied to retail credit loans. Finally, under a point process filtering framework, we extend the standard discrete-time filtering formula to a more general setting, where the hidden process does not need to be a Markov chain. © 2023 World Scientific Publishing Company.

关键词： economic cycle em algorithms filtering Markov chain Rating transitions

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Large-scale estimation of random graph models with local dependence

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年 152卷 107029-107029页

作者： Babkin, Sergii Stewart, Jonathan R. Long, Xiaochen Schweinberger, Michael Microsoft Redmond WA USA Florida State Univ Dept Stat Tallahassee FL 32306 USA Rice Univ Dept Stat 6100 Main St Houston TX 77005 USA

A class of random graph models is considered, combining features of exponential-family models and latent structure models, with the goal of retaining the strengths of both of them while reducing the weaknesses of each of them. An open problem is how to estimate such models from large networks. A novel approach to large-scale estimation is proposed, taking advantage of the local structure of such models for the purpose of local computing. The main idea is that random graphs with local dependence can be decomposed into subgraphs, which enables parallel computing on subgraphs and suggests a two-step estimation approach. The first step estimates the local structure underlying random graphs. The second step estimates parameters given the estimated local structure of random graphs. Both steps can be implemented in parallel, which enables large-scale estimation. The advantages of the two-step estimation approach are demonstrated by simulation studies with up to 10,000 nodes and an application to a large Amazon product recommendation network with more than 10,000 products. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Exponential random graph models Latent structure models Stochastic block models Variational methods em algorithms MM algorithms

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Generalisations of stochastic supervision models

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PATTERN RECOGNITION 2021年 109卷 107575-107575页

作者： Lu, Xiaoou Qiao, Yangqi Zhu, Rui Wang, Guijin Ma, Zhanyu Xue, Jing-Hao UCL Dept Stat Sci London WC1E 6BT England Univ London Fac Actuarial Sci & Insurance Cass Business Sch London EC1Y 8TZ England Univ Kent Sch Math Stat & Actuarial Sci Canterbury CT2 7FS Kent England Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China

When the labelling information is not deterministic, traditional supervised learning algorithms cannot be applied. In this case, stochastic supervision models provide a valuable alternative to classification. However, these models are restricted in several aspects, which critically limits their applicability. In this paper, we provide four generalisations of stochastic supervision models, extending them to asymmetric assessments, multiple classes, feature-dependent assessments and multi-modal classes, respectively. Corresponding to these generalisations, we derive four new em algorithms. We show the effectiveness of our generalisations through illustrative examples of simulated datasets, as well as real-world examples of three famous datasets, the MNIST dataset, the CIFAR-10 dataset and the emNIST dataset. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： em algorithms Imperfect supervision Finite mixture model Stochastic supervision

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Statistical inference with incomplete and high-dimensional data - modeling polytraumatized patients: Inférence statistique avec des données incomplètes et de grandes dimensions - modélisation des polytraumatisés graves

Statistical inference with incomplete and high-dimensional d...

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作者： Jiang, Wei Université Paris-Saclay

学位级别：博士

Le problème des données manquantes existe depuis les débuts de l'analyse des données, car les valeurs manquantes sont liées au processus d'obtention et de préparation des donn... 详细信息

Le problème des données manquantes existe depuis les débuts de l'analyse des données, car les valeurs manquantes sont liées au processus d'obtention et de préparation des données. Dans les applications des statistiques modernes et de l'apprentissage machine, où la collecte de données devient de plus en plus complexe et où de multiples sources d'information sont combinées, les grandes bases de données présentent souvent un nombre extraordinairement élevé de valeurs manquantes. Ces données présentent donc d'importants défis méthodologiques et techniques pour l'analyse : de la visualisation à la modélisation, en passant par l'estimation, la sélection des variables, les capacités de prédiction et la mise en oeuvre par des implémentations. De plus, bien que les données en grande dimension avec des valeurs manquantes soient considérées comme des difficultés courantes dans l'analyse statistique aujourd'hui, seules quelques solutions sont disponibles.L'objectif de cette thèse est de développer de nouvelles méthodologies pour effectuer des inférences statistiques avec des données manquantes et en particulier pour des données en grande dimension. La contribution la plus importante est de proposer un cadre complet pour traiter les valeurs manquantes, de l'estimation à la sélection d'un modèle, en se basant sur des approches de vraisemblance. La méthode proposée ne repose pas sur un dispositif spécifique du manque, et permet un bon équilibre entre qualité de l'inférence et implémentations *** contributions de la thèse se composent en trois parties. Dans le chapitre 2, nous nous concentrons sur la régression logistique avec des valeurs manquantes dans un cadre de modélisation jointe, en utilisant une approximation stochastique de l'algorithme em. Nous étudions l'estimation des paramètres, la sélection des variables et la prédiction pour de nouvelles observations incomplètes. Grâce à des simulations complètes, nous montrons que les estimateurs sont non biaisés et ont de

关键词： Observations manquantes (statistique) Modèles linéaires généralisés Algorithmes em Analyse de régression Dépendance (statistique) Statistique médicale Missing Observations Generalized Linear Models em algorithms Regression Analysis Dependence Medical Statistics

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Post-processing Multiensemble Temperature and Precipitation Forecasts Through an Exchangeable Normal-Gamma Model and Its Tobit Extension

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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2019年第2期24卷 309-345页

作者： Courbariaux, Marie Barbillon, Pierre Perreault, Luc Parent, Eric Univ Paris Saclay INRA AgroParisTech UMR MIA Paris F-75005 Paris France Hydro Quebec Res Inst Varennes PQ Canada

Meteorological ensemble members are a collection of scenarios for future weather issued by a meteorological center. Such ensembles nowadays form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess point-wise estimate. Unfortunately, ensemble members cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to re-calibrate the ensemble. Two main families of post-processing methods, either competing such as the BMA or collaborative such as the emOS, can be found in the literature. This paper proposes a mixed-effect model belonging to the collaborative family. The structure of the model is formally justified by Bruno de Finetti's representation theorem which shows how to construct operational statistical models of ensemble based on judgments of invariance under the relabeling of the members. Its interesting specificities are as follows: (1) exchangeability contributes to parsimony, with an interpretation of the latent pivot of the ensemble in terms of a statistical synthesis of the essential meteorological features of the ensemble members, (2) a multiensemble implementation is straightforward, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto normal statistical structures, first with a direct application for temperatures, then with its very convenient Tobit extension for precipitation. Inference is performed by expectation maximization (em) algorithms with both steps leading to explicit analytic expressions in the Gaussian temperature case, and recourse is made to stochastic conditional simulations in the zero-inflated precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation e

关键词： Hierarchical latent variable models em algorithms Ensemble numerical weather prediction Statistical post-processing Temperature Precipitation

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Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年 151卷 107010-107010页

作者： Philipson, Pete Hickey, Graeme L. Crowther, Michael J. Kolamunnage-Dona, Ruwanthi Newcastle Univ Sch Math Stat & Phys Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Liverpool Inst Translat Med Dept Biostat Liverpool Merseyside England Univ Leicester Dept Hlth Sci Biostat Res Grp Leicester Leics England

Quasi-Monte Carlo (QMC) methods using quasi-random sequences, as opposed to pseudo-random samples, are proposed for use in the joint modelling of time-to-event and multivariate longitudinal data. The QMC integration framework extends the Monte Carlo Expectation Maximisation approaches that are commonly adopted, namely using ordinary and antithetic variates. The motivation of QMC integration is to increase the convergence speed by using nodes that are scattered more uniformly. Through simulation, estimates and computational times are compared and this is followed with an application to a clinical dataset. There is a distinct speed advantage in using QMC methods for small sample sizes and QMC is comparable to the antithetic MC method for moderate sample sizes. The new method is available in an updated version of the R package joineRML. Crown Copyright (C) 2020 Published by Elsevier B.V. All rights reserved.

关键词： Quasi Monte Carlo Joint modelling Multivariate longitudinal Time-to-event em algorithms

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A subjectivity-aware algorithm for label aggregation in crowdsourcing 22

A subjectivity-aware algorithm for label aggregation in crow...

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22nd IEEE International Conference on Computational Science and Engineering (IEEE CSE) / 17th IEEE International Conference on embedded and Ubiquitous Computing (IEEE EUC)

作者： Wu, Ming Li, Qianmu Wang, Shuo Hou, Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Wuyi Univ Intelligent Mfg Dept Jiangmen Peoples R China

ISBN: (纸本)9781728116648

Crowdsourcing has already attracted a wide attention in the field of machine learning and its related fields. A large amount of labeled data can be obtained quickly and cheaply on crowdsourcing platforms. To deal with the problem that labels collected from crowds are usually noisy due to the low accuracy of non-expert online workers, we use quality control methods to improve the qualities of crowd data. Unfortunately, current quality control methods only consider the instance difficulty or the worker reliability to account for the variety of labels to the same instance, and these methods did not take subjectivity of workers into consideration which also effects the responses. In this paper, we present a novel subjectivity-aware algorithm for label aggregation, which also model the difficulty of instances and reliability of workers as latent parameters. This method is an em-like algorithm, which not only infers the ground truth of the instances, but also simultaneously estimates the latent parameters. Experimental results on real-world datasets show that our method outperforms the state-of-the-art ground truth inference algorithms.

关键词： crowdsourcing quality control subjectivity difficulty reliability em algorithms

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Non-parametric methodologies for reconstruction and estimation in nonlinear state-space models

Non-parametric methodologies for reconstruction and estimati...

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作者： Thi Tuyet Trang Chau UNIVERSITE DE RENNES 1

学位级别：博士

The amount of both observational and model- simulated data within the environmental, climate and ocean sciences has grown at an accelerating rate. Observational (e. g. satellite, in-situ...) data are generally accurate but still subject to observational errors and available with a complicated spatio-temporal sampling. Increasing computer power and understandings of physical processes have permitted to advance in models accuracy and resolution but purely model driven solutions may still not be accurate enough. Filtering and smoothing (or sequential data assimilation methods) have developed to tackle the issues. Their contexts are usually formalized under the form of a space-state model including the dynamical model which describes the evolution of the physical process (state), and the observation model which describes the link between the physical process and the available observations. In this thesis, we tackle three problems related to statistical inference for nonlinear state-space models: state reconstruction, parameter estimation and replacement of the dynamic model by an emulator constructed from data. For the first problem, we will introduce an original smoothing algorithm which combines the Conditional Particle Filter (CPF) and Backward Simulation (BS) algorithms. This CPF-BS algorithm allows for efficient exploration of the state of the physical variable, sequentially refining exploration around trajectories which best meet the constraints of the dynamic model and observations. We will show on several toy models that, at the same computation time, the CPF-BS algorithm gives better results than the other CPF algorithms and the stochastic EnKS algorithm which is commonly used in real applications. We will then discuss the problem of estimating unknown parameters in state-space models. The most common statistical algorithm for estimating the parameters of a space-state model is based on em algorithm, which makes it possible to iteratively compute a numerical ap

关键词： non-parametric estimation em algorithms local regression conditional particle filtering smoothing nonlinear state-space models

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Flow-Process Foreground Region of Interest Detection Method for Video Codecs

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IEEE ACCESS 2017年 5卷 16263-16276页

作者： Zhang, Zhewei Jing, Tao Han, Jingning Xu, Yaowu Li, Xuejing Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Google Inc Mountain View CA 94043 USA

Detecting the foreground region of interest (ROI) for video sequences is an important issue both for video codecs and monitoring systems. In this paper, we propose a flow-process-based method to detect foreground ROI using four steps: global motion compensation, motion block extraction, multi-layer segmentation, and model updating. The former two procedures extract the foreground motion blocks and form a motion mask, and the latter two procedures remove the pixels belonging to the background inside the motion mask and update the color distributions of the background model. In addition, a block-based to pixel-based detection scheme is proposed to allow detection flexibility. Another benefit of the proposed method is that it can be embedded in video codecs for real-time ROI detection and encoding. Experimental results demonstrate that our method achieves improved performance in terms of both detection accuracy and time consumption.

关键词： Foreground ROI detection video codecs image segmentation global motion detection Markov random field em algorithms machine learning

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