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Your search keyword '"Mixture transition model"' showing total 2 results
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2 results on '"Mixture transition model"'

1. A Truncated Mixture Transition Model for Interval-Valued Time Series.

Author

Luo, Yun and González-Rivera, Gloria

Subjects
RATE of return on stocks, EXPECTATION-maximization algorithms, GAUSSIAN distribution, TIME series analysis, HETEROSCEDASTICITY
Abstract

We propose a model for interval-valued time series that specifies the conditional joint distribution of the upper and lower bounds as a mixture of truncated bivariate normal distributions. It preserves the interval natural order and provides great flexibility on capturing potential conditional heteroscedasticity and non-Gaussian features. The standard expectation maximization (EM) algorithm applied to truncated mixtures does not provide a closed-form solution in the M step. A new EM algorithm solves this problem. The model applied to the interval-valued IBM daily stock returns exhibits superior performance over competing models in-sample and out-of-sample evaluation. A trading strategy showcases the usefulness of our approach. [ABSTRACT FROM AUTHOR]

Published
2024
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2. A new statistical approach to network anomaly detection

Author

CHRISTIAN CALLEGARI, Vaton, S., Pagano, M., TLC Network Research Group (TLCNETGRP), University of Pisa - Università di Pisa, Département informatique (INFO), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), and Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque

Subjects
[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP], Intrusion detection system, High order Markov chain, Non-homogeneous Markov chain, Mixture transition model, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR], Computer Science::Cryptography and Security
Abstract

International audience; In the last few years, the number and impact of security attacks over the Internet have been continuously increasing. To face this issue, the use of Intrusion Detection Systems (IDSs) has emerged as a key element in network security. In this paper we address the problem considering a novel statistical technique for detecting network anomalies. Our approach is based on the use of different families of Markovian models (namely high order and non homogeneous Markov chains) for modeling network traffic running over TCP. The performance results shown in the paper, justify the proposed method and highlight the improvements over commonly used statistical techniques.

Published
2008

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