Detection of multiple change-points in multivariate data

    Authors

    E. M. Maboudou-Tchao;D. M. Hawkins

    Comments

    Authors: contact us about adding a copy of your work at [email protected]

    Abbreviated Journal Title

    J. Appl. Stat.

    Keywords

    regression trees; binary splitting; principle of optimality; separability; dynamic programming; DNA-SEQUENCE SEGMENTATION; TIME-SERIES; MODELS; PARAMETERS; Statistics & Probability

    Abstract

    The statistical analysis of change-point detection and estimation has received much attention recently. A time point such that observations follow a certain statistical distribution up to that point and a different distribution - commonly of the same functional form but different parameters after that point - is called a change-point. Multiple change-point problems arise when we have more than one change-point. This paper develops a method for multivariate normally distributed data to detect change-points and estimate within-segment parameters using maximum likelihood estimation.

    Journal Title

    Journal of Applied Statistics

    Volume

    40

    Issue/Number

    9

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    1979

    Last Page

    1995

    WOS Identifier

    WOS:000323919600010

    ISSN

    0266-4763

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