Tests for a Change-Point

James, Barry R.; James, Kang Ling; Siegmund, David

doi:10.2307/2336022

Cited by 22 publications

(28 citation statements)

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“…In offline methods the entire data set is observed and can be processed at once. The off-line problems are growing fast and have attracted many researchers in computational biology and biostatistics as well as offline computer vision applications [7][8][9][10][11][12][13][14]. In previous work [15,16], often Cross Validation (CV) is used to select the bandwidth to locate the discontinuities in the observed signal including inflection points, change points, jumps and valleys.…”

Section: Introductionmentioning

confidence: 99%

Non-Parametric Estimation of a Single Inflection Point in Noisy Observed Signal

Kachouie¹,

Schwartzman²

2013

J Elec Electron Syst

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R s ∈ , and r is an unknown underlying regression function. The regression function r can be locally approximated at the point s by a polynomial of order p AbstractInflection point detection is an important yet challenging problem in science and engineering. This paper addresses the estimation of a single inflection point location in noisy observations using non-parametric polynomial regression. To address the bandwidth problem, a constrained approach is proposed to ensure having a single inflection point, thereby reducing the uncertainty in the inflection point location whereas being flexible on the shape of the underlying signal. The performance of the proposed method is evaluated through simulations. It is shown that the proposed method can effectively estimate the inflection point under high noise conditions.

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Section: Introductionmentioning

confidence: 99%

Non-Parametric Estimation of a Single Inflection Point in Noisy Observed Signal

Kachouie¹,

Schwartzman²

2013

J Elec Electron Syst

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“…Sen and Srivastava 1, 2 , Hawkins 3 , Worsley 4 , and James et al 5 considered tests for mean shifts of normal i.i.d. sequences.…”

Section: Introductionmentioning

confidence: 99%

Testing for Change in Mean of Independent Multivariate Observations with Time Varying Covariance

Boutahar

2012

Journal of Probability and Statistics

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We consider a nonparametric CUSUM test for change in the mean of multivariate time series with time varying covariance. We prove that under the null, the test statistic has a Kolmogorov limiting distribution. The asymptotic consistency of the test against a large class of alternatives which contains abrupt, smooth and continuous changes is established. We also perform a simulation study to analyze the size distortion and the power of the proposed test.

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“…Change-point problems have been addressed in the framework of hypothesis testing (Hinkley 1971;Hinkley and Hinkley 1970;Pettitt 1980;James et al 1987;Raftery and Akman 1986) and model estimation (Smith 1975;Raftery and Akman 1986;Barry and Hartigan 1993;Stephens 1994). Particularly, in the Bayesian paradigm, change-points are regarded as either model parameters that are subject to posterior sampling (Smith 1975;Raftery and Akman 1986;Barry and Hartigan 1993;Stephens 1994), or a model's special characteristics to be sought via model selection procedures (e.g., Raftery and Akman 1986;Denison et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Change-point diagnostics in competing risks models: Two posterior predictive p-value approaches

Wang

Ghosh

2007

TEST

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This paper presents a Bayesian diagnostic procedure for examining change-point assumption in the competing risks model framework. It considers the family of distributions arising from the cause-specific model as reported by Chiang (Introduction to stochastic processes in biostatistics. Wiley, New York, 1968) upon which change-points are added to accommodate possible distributional heterogeneity. Model departure, due to misspecification of change-points associated with either the overall survival distribution or cause-specific probabilities, is quantified in terms of a sequence of cumulative-sum statistics between each pair of adjacent change-points assumed. When assessing the asymptotic behavior of each sequence of cumulative-sum statistics using its posterior predictive p-values, see Rubin (Ann Stat 12:1151-1172, 1984 and partial posterior predictive p-values as reported by Bayarri and Berger (J Am Stat Assoc 95:1127-1142, we show that both types of p-values attain their greatest departure from 0.5 at the change-point that is missed in the assumed model, from which a diagnostic procedure is formalized. Statistical power of these two types of p-values is discussed.

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Tests for a Change-Point

Cited by 22 publications

References 0 publications

Non-Parametric Estimation of a Single Inflection Point in Noisy Observed Signal

Non-Parametric Estimation of a Single Inflection Point in Noisy Observed Signal

Testing for Change in Mean of Independent Multivariate Observations with Time Varying Covariance

Change-point diagnostics in competing risks models: Two posterior predictive p-value approaches

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