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A Fisher-scoring algorithm for fitting latent class models with individual covariates

ECONOMETRICS AND STATISTICS 2017年 3卷 132-140页

作者： Forcina, Antonio Dipartimento Econ Via Pascoli I-06100 Perugia Italy

Describes a modified Fisher scoring algorithm for fitting a wide variety of latent class models for categorical responses when both the class weights and the conditional distributions of the responses depend on individual covariates through a multinomial logit. A simple expression for computing the score vector and the empirical information matrix is presented;it is shown that this matrix is positive definite under mild conditions. The Fisher scoring algorithm combines the empirical information matrix to update the step direction with a line search to optimize the step length. The algorithm converges for almost any choice of starting values. An application to the field of education transmission seems to suggest that, while parents' education affects the child latent ability, their pressure affects directly the child's achievements. (c) 2016 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

关键词： Categorical data analysis em algorithms empirical information matrix Fisher scoring algorithms Individual covariates Latent class models Line search multinomial logit

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Improved T-Cluster Based Scheme for Combination Gene Scale Expression Data

Improved T-Cluster Based Scheme for Combination Gene Scale E...

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International Conference on Radar, Communication and Computing (ICRCC)

作者： Vengatesan, K. Selvarajan, S. Muthayammal Engn Coll Dept Comp Sci & Engn Namakkal Tamil Nadu India Muthayammal Tech Campus Rasipuram Namakkal Tamil Nadu India

ISBN: (纸本)9781467327589;9781467327565

Clustering is an unsupervised learning technique in that there is no explicit demarcation of data as training and test data. Clustering aims to group related records by measuring similarities among the attribute. Major phase of clustering techniques is similarity measurement and it is based on different factors and parameters. The improved Nonnegative Matrix Factorization (NMF) based TCLUST (T-Clustering) algorithm is em principle (Expectation Maximization) based algorithm, intended to search for approximate solutions. The em algorithm is the efficient method of obtaining a solution to the mixture likelihood problem. Genes with a common function are often hypothesized to have correlated expression levels across different conditions. NMF clustering is introduced to find a small number of Meta genes, each defined as a positive linear combination of the genes in the expression data. The proposed clustering algorithm is applied to a genome scale gene expression dataset to enrichment analysis and to discover highly significant biological clusters.

关键词： TCLUST Non Negative Matrix Factorization em algorithms Translation Tanimoto Correlation Coefficient

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A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network

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International Journal of Information Technology (Singapore) 2022年第4期14卷 2021-2033页

作者： Kalnoor, Gauri Gowrishankar, S. BMS College of Engineering Bangalore India

Significant growth of smart home devices is adopted, which provides security, convenience, and energy efficiency for users, in recent years. As an instance, consider a secured smart camera that detects movements of unauthorized objects, whereas the fire accidents can be detected by smoke sensors. Though, a surface for new cyber threats is some of the most recent examples that are open up in this field. Furthermore, recent examples also show that the distributed denial of service (DDoS) attacks are performed by misusing the smart devices which are hacked violating the privacy rules. In this proposed work, the application of machine learning in an environment of smart homes is explored so that the anomalous activities that occur can be identified. The sensor data at the network level is trained using a model based on the Markov chain known as hidden Markov model (HMM) and is created using smart devices and multiple sensors from a testbed. The model generated using HMM achieves 97% accuracy while detecting potential anomalies where attacks are indicated. In this approach, we construct the model and differentiate the analysed results with existing techniques. The starting step for securing IoT networks is intrusion detection and the next step is the intruders prediction which provides an active defence against the incoming attacks. The model employs an algorithm that does not depend on specific domain knowledge. The work has achieved an improvement in prediction accuracy of 5% for an alert category over the current variable length methods of Markov chain intrusion prediction, as they provided information more for a possible defence. The DDoS attack is considered as a coordinated attack mainly carried out based on a large scale depending on whether the target system’s resources or services are available. Thus, the novel approach also describes a novel machine learning-based technique using an algorithm known as variational dynamic Bayesian algorithm which helps to obtain

关键词： DDoS Dynamic Bayesian networks em algorithms Gibbs sampling Hidden Markov models Network attack State space models Variational Bayesian Variational methods

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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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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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Machine Learning for Flow Cytometry Data Analysis

Machine Learning for Flow Cytometry Data Analysis

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作者： Lee, Gyemin University of Michigan

学位级别：Ph.D.

This thesis concerns the problem of automatic flow cytometry data analysis. Flow cytometry is a technique for rapid cell analysis and widely used in many biomedical and clinical laboratories. Quantitative measurements from a flow cytometer provide rich information about various physical and chemical characteristics of a large number of cells. In clinical applications, flow cytometry data is visualized on a sequence of two-dimensional scatter plots and analyzed through a manual process called "gating". This conventional analysis process requires a large amount of time and labor and is highly subjective and inefficient. In this thesis, we present novel machine learning methods for flow cytometry data analysis to address these issues. We first begin by a method for generating a high dimensional flow cytometry dataset from multiple low dimensional datasets. We present an imputation algorithm based on clustering and show that it improves upon a simple nearest neighbor based approach that often induces spurious clusters in the imputed data. This technique enables the analysis of multi-dimensional flow cytometry data beyond the fundamental measurement limits of instruments. We then present two machine learning methods for automatic gating problems. Gating is a process of identifying interesting subsets of cell populations. Pathologists make clinical decisions by inspecting the results from gating. Unfortunately, this process is performed manually in most clinical settings and poses many challenges in high-throughput analysis. The first approach is an unsupervised learning technique based on multivariate mixture models. Since measurements from a flow cytometer are often censored and truncated, standard model-fitting algorithms can cause biases and lead to poor gating results. We propose novel algorithms for fitting multivariate Gaussian mixture models to data that is truncated, censored, or truncated and censored. Our second approach is a transfer learning technique combine

关键词： Machine Learning Flow Cytometry Support Vector Machine Mixture Models em algorithms Statistical File Matching Thesis

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Mutual kernel matrix completion

Mutual kernel matrix completion

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IEICE Transactions on Information and Systems

作者： Rivero, Rachelle Lemence, Richard Kato, Tsuyoshi Department of Computer Science Gunma University Kiryu-shi376-8515 Japan Institute of Mathematics College of Science University of the Philippines Diliman Quezon City1101 Philippines

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods- a way in which one can represent heterogeneous data sets into a single form: as kernel matrices. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the em algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the (completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices. Copyright © 2017 The Institute of Electronics, Information and Comm

关键词： Matrix algebra Bioinformatics Classification (of information) Data fusion Data mining Support vector machines Classification tasks em algorithms Heterogeneous data Kernel matrices Kullback Leibler divergence Missing information Multiple kernels Objective functions

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Demand Estimation with Missingness of Product Availability

Demand Estimation with Missingness of Product Availability

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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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Spatio-temporal mixture process estimation to detect dynamical changes in population

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 126卷 102258-102258页

作者： Pruilh, Solange Jannot, Anne-Sophie Allassonniere, Stephanie Ecole Polytech Ctr Appl Math Route Saclay F-91128 Palaiseau France Sorbonne Univ Univ Paris INRIA INSERMUMR S1138 Paris France Hop Europeen Georges Pompidou AP HP Dept Stat Med Informat & Publ Hlth Paris France

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatiotemporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (em) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing methods on several simulated datasets. We then combine the algorithm with a temporal statistical model, allowing for the detection of dynamical changes in population distributions, and call the result a spatio-temporal mixture process (STMP). We test STMPs on synthetic data, and consider several different behaviors of the distributions, to fit this process. Finally, we validate STMPs on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.

关键词： Gaussian Mixture Model em algorithms Spatio-temporal data

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Data-driven modeling of the temporal evolution of breakers' states in the French electrical transmission grid?

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NONLINEAR ANALYSIS-HYBRID SYSTemS 2022年第0期46卷

作者： Gonzalez, Mauricio Girard, Antoine EthiFinance EthiFinance Analyt 11 Ave Delcasse F-75008 Paris France Univ Paris Saclay CNRS CentraleSupelec L2S 3 rue Joliot Curie F-91190 Gif Sur Yvette France

In electrical transmission grids, it is common to observe the states of circuit breakers. While they are known at irregular times, system modeling and grid state estimation are of the highest importance to ensure secure operations. This paper proposes a richer method to estimate the grid state over its reference configurations based on the temporal evolution of its breakers' states. The first contribution consists in developing a general multi-observation continuous-time finite-state Hidden Markov Model with filter-based parameter estimation to infer the hidden state (e.g., the grid reference configuration) handling multiple observed processes with irregular ''jump"times (e.g., the breakers' states). As a second contribution, we build a numerical scheme with no discretization error adapted to all state jumps generated by the observed processes. Finally, we apply our model to simulated and real data to illustrate the approach's performance. The available data consists of historical records of breakers' states during the electrical transmission grid operated normally. For this real-data-driven application, we also present a clustering approach to identify the set of grid reference configurations. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Data-driven modeling Hidden Markov Models em algorithms Electrical transmission grid

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