The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform
Bruno Portier - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions
SIAM Journal on Control and Optimization, 2020Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine lea...
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An efficient stochastic Newton algorithm for parameter estimation in logistic regressions
2019Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine learning, public health, social sciences, ecology and econometry. In order to estimate the unknown parameters of logistic regression with data streams arriving sequentially and at high speed, we focus our attention on a recursive stochastic algorithm. More precisely, we investigate the asymptotic behavior of a new stochastic Newton algorithm. It enables to easily update the estimates when the data arrive sequentially and to have research steps in all directions. We establish the almost sure convergence of our stochastic Newton algorithm as well as its asymptotic normality. All our theoretical results are illustrated by numerical experiments.
Bernard Bercu - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions
SIAM Journal on Control and Optimization, 2020Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine lea...
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An efficient stochastic Newton algorithm for parameter estimation in logistic regressions
2019Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine learning, public health, social sciences, ecology and econometry. In order to estimate the unknown parameters of logistic regression with data streams arriving sequentially and at high speed, we focus our attention on a recursive stochastic algorithm. More precisely, we investigate the asymptotic behavior of a new stochastic Newton algorithm. It enables to easily update the estimates when the data arrive sequentially and to have research steps in all directions. We establish the almost sure convergence of our stochastic Newton algorithm as well as its asymptotic normality. All our theoretical results are illustrated by numerical experiments.
Antoine Godichon - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions
SIAM Journal on Control and Optimization, 2020Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine lea...
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An efficient stochastic Newton algorithm for parameter estimation in logistic regressions
2019Co-Authors: Bernard Bercu, Antoine Godichon, Bruno PortierAbstract:Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a Binary Random Variable. It has a wide range of applications including machine learning, public health, social sciences, ecology and econometry. In order to estimate the unknown parameters of logistic regression with data streams arriving sequentially and at high speed, we focus our attention on a recursive stochastic algorithm. More precisely, we investigate the asymptotic behavior of a new stochastic Newton algorithm. It enables to easily update the estimates when the data arrive sequentially and to have research steps in all directions. We establish the almost sure convergence of our stochastic Newton algorithm as well as its asymptotic normality. All our theoretical results are illustrated by numerical experiments.
Jakob Dahl Andersen - One of the best experts on this subject based on the ideXlab platform.
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Optimal context quantization in lossless compression of image data sequences
IEEE Transactions on Image Processing, 2004Co-Authors: Soren Forchhammer, Xiaolin Wu, Jakob Dahl AndersenAbstract:In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C/sub t/=(X/sub t-t1/,X/sub t-t2/,...,X/sub t-tK/) of a source symbol X/sub t/ into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a Binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a Random Variable can be decomposed into a sequence of Binary decisions, each of which is coded using optimal context quantization designed for the corresponding Binary Random Variable. This optimized coding scheme is applied to digital maps and /spl alpha/-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
Farzin Modarres Khiyabani - One of the best experts on this subject based on the ideXlab platform.
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A New Stochastic Model for Classifying Flexible Measures in Data Envelopment Analysis
Journal of the Operations Research Society of China, 2020Co-Authors: Mansour Sharifi, Ghasem Tohidi, Behrouz Daneshian, Farzin Modarres KhiyabaniAbstract:The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units (DMUs) motivates new perspectives in modeling and solving data envelopment analysis (DEA) in the presence of flexible Variables. Because the orientation of flexible data is not pre-determined, and because the number of DMUs is fixed and all the DMUs are independent, flexible data can be treated as Random Variable in terms of both input and output selection. As a result, the selection of flexible Variable as input or output for n DMUs can be regarded as Binary Random Variable. Assuming the Randomness of choosing flexible data as input or output, we deal with DEA models in the presence of flexible data whose input or output orientation determines a binomial distribution function. This study provides a new insight to classify flexible Variable and investigates the input or output status of a Variable using a stochastic model. The proposed model obviates the problems caused by the use of the large M number and using its different values in previous models. In addition, it can obtain the most appropriate efficiency value for decision-making units by assigning the chance of choosing the orientation of flexible Variable to the model itself. The proposed method is compared with other available methods by employing numerical and empirical examples.