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Showing 1 to 9 of 9 for “"Change-point problem"”.
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On testing the change-point in the longitudinal bent line quantile regression model
The problem of detecting changes has been receiving considerable attention in various fields. In general, the change-point problem is to identify the location(s) in an ordered sequence that divides this sequence into groups, which follow different models. This dissertation considers the …
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Mehrdimensionale Change-Point-Schätzung mit U-Statistiken
Wir betrachten ein mehrdimensionales Change-Point-Problem. Seien X1;n; : : : ;Xn;n unabhängige Zufallselemente bei denen q, q 2 N, Verteilungswechsel auftreten. Dass heisst, es existiert ein Vektor µ = (µ1; : : : ; µq) 2 Rq mit 0 = µ0 < µ1 < ¢ ¢ ¢ < µq < µq+1 = 1 sowie Verteilungen …
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A Nonparametric Method for Ascertaining Change Points in Regression Regimes
Of interest is the specific model called the joinpoint two regime regression or broken line model composed of one regression line and a horizontal ray. This is a very restricted but highly useful subset of the well-researched change point problem. The usual approach to a more general model was …
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Detecting change-points in a Compound Poisson Process
A statistical change point problem was first studied in the mid-1950s in the context of quality control in industrial processes. A change point is defined as a point in the time order when the probability distribution of a sequence of observations differs before and after that point. The literature …
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Statistical learning methods for mining marketing and biological data
… model real world data with temporal or spatial changes. First, a collaborated online change-point detection method is proposed to identify the change-points in sparse time series. It leverages the signals from the auxiliary time series such as engagement metrics to compensate the sparse revenue …
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Inference and Estimation in Change Point Models for Censored Data
<p>In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields and can also apply to survival data. With improvements to medical diagnoses and treatments, incidences and mortality …
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Statistical inference for dependent data
… through statistical downscaling, we consider the change point problem and the two sample problem for temporally dependent functional data. Specifically, in Chapter 1, we develop a self-normalization based test to test the structural stability of temporally dependent functional observations. We …
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Approximation of Quantiles of Rank Test Statistics Using Almost Sure Limit Theorems
<p>There are many problems in statistics where the analysis is based on asymptotic distributions. In some cases, the asymptotic distribution is in an open form or is intractable. One possible solution is the logarithmic quantile estimation (LQE) method introduced by Thangavelu (2005) for rank tests …
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High-dimensional change point detection for mean and location parameters
Change point inference refers to detection of structural breaks of a sequence observation, which may have one or more distributional shifts subject to models such as mean or covariance changes. In this dissertation, we consider the offline multiple change point problem that the sample size is fixed …