Change point estimation in high dimensional Markov random‐field models
نویسندگان
چکیده
منابع مشابه
Change point estimation in high dimensional Markov random-field models.
This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logari...
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Let {X(t), 1 ≤ t ≤ T} be a sequence of Rp-valued independent random variables. Let Θ ⊆ Rd be an open, non-empty convex parameter space equipped with the Euclidean inner product 〈·, ·〉, and norm‖ · ‖2. We will also use the `1-norm ‖θ‖1 def = ∑d j=1 |θj |, and the `∞-norm ‖θ‖∞ def = max1≤j≤d |θj |. We assume that there exists a change point τ? ∈ {1, . . . , T − 1}, parameters θ ? , θ (2) ? ∈ Θ, s...
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Changepoints are a very common feature of Big Data that arrive in the form of a data stream. In this paper, we study high-dimensional time series in which, at certain time points, the mean structure changes in a sparse subset of the coordinates. The challenge is to borrow strength across the coordinates in order to detect smaller changes than could be observed in any individual component series...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2016
ISSN: 1369-7412,1467-9868
DOI: 10.1111/rssb.12205