Potential Change Point Research Articles

Provider Adherence to the 2020 US Multi-Society Task Force Guidelines for Surveillance After Colonoscopy With Polypectomy

Background and AimsThe 2020 United States Multi-Society Task Force on Colorectal Cancer guidelines for surveillance after colonoscopy with polypectomy introduced significant changes in surveillance intervals. We sought to identify rates of adherence to these new guidelines at an academic medical center. MethodsAverage-risk screening colonoscopies where 1-4 polyps <10 mm were removed between 1/1/2020 and 6/30/21 were included. To determine predictors of non-adherence, a multivariable logistic regression analysis was conducted and included patient and procedure-related variables. Bayesian changepoint analysis was applied to identify timing of change in adherence. Multinomial logistic regression was used to identify predictors of variability within the guidelines. Results1,026 procedures were analyzed. Adherence to the guidelines was 85%. In procedures with 1-2 polyps, increasing size (OR 0.84 per mm; 95% CI: 0.75 - 0.93) and a mixture of tubular adenomas (TA) and hyperplastic polyps (HP) (OR 0.35; 95% CI: 0.21 - 0.59) were associated with non-adherence. Among procedures with 1-2 TAs, age (OR 0.86 per 5-year increase; 95% CI: 0.75 - 0.99), increasing size (OR 0.86 per mm; 95% CI: 0.75 - 0.99) and polyp number (OR 0.54; 95% CI: 0.33-0.90) were associated with non-adherence. Changepoint analysis identified a potential changepoint on 2/9/20 (95% credible interval 2/4/2020-2/22/2020). Larger TAs were less likely to receive a 10 year recommendation (OR 0.61, 95% CI 0.5 – 0.75). ConclusionAdherence rates to the 2020 surveillance guidelines were high with a potential changepoint identified on 2/9/20. Deviation from guidelines was associated with patient and procedure related variables.

Test of change point versus long‐range dependence in functional time series

In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated as well. The test statistic is constructed using the local Whittle estimator applied to these coefficients. It has standard normal distribution under the null hypothesis (change point) and diverges to infinity under the alternative (long range dependence). The article includes asymptotic theory, a simulation study and an application to curve‐valued time series derived from intraday asset prices.

Study on a mother wavelet optimization framework based on change-point detection of hydrological time series

Abstract. Hydrological time series (HTS) are the key basis of water conservancy project planning and construction. However, under the influence of climate change, human activities and other factors, the consistency of HTS has been destroyed and cannot meet the requirements of mathematical statistics. Series division and wavelet transform are effective methods to reuse and analyse HTS. However, they are limited by the change-point detection and mother wavelet (MWT) selection and are difficult to apply and promote in practice. To address these issues, we constructed a potential change-point set based on a cumulative anomaly method, the Mann–Kendall test and wavelet change-point detection. Then, the degree of change before and after the potential change point was calculated with the Kolmogorov–Smirnov test, and the change-point detection criteria were proposed. Finally, the optimization framework was proposed according to the detection accuracy of MWT, and continuous wavelet transform was used to analyse HTS evolution. We used Pingshan station and Yichang station on the Yangtze River as study cases. The results show that (1) change-point detection criteria can quickly locate potential change points, determine the change trajectory and complete the division of HTS and that (2) MWT optimal framework can select the MWT that conforms to HTS characteristics and ensure the accuracy and uniqueness of the transformation. This study analyses the HTS evolution and provides a better basis for hydrological and hydraulic calculation, which will improve design flood estimation and operation scheme preparation.

A Bivariate Beta from Gamma Ratios for Determining a Potential Variance Change Point: Inspired from a Process Control Scenario

Within statistical process control (SPC), normality is often assumed as the underlying probabilistic generator where the process variance is assumed equal for all rational subgroups. The parameters of the underlying process are usually assumed to be known—if this is not the case, some challenges arise in the estimation of unknown parameters in the SPC environment especially in the case of few observations. This paper proposes a bivariate beta type distribution to guide the user in the detection of a permanent upward or downward step shift in the process’ variance that does not directly rely on parameter estimates, and as such presents itself as an attractive and intuitive approach for not only potentially identifying the magnitude of the shift, but also the position in time where this shift is most likely to occur. Certain statistical properties of this distribution are derived and simulation illustrates the theoretical results. In particular, some insights are gained by comparing the newly proposed model’s performance with an existing approach. A multivariate extension is described, and useful relationships between the derived model and other bivariate beta distributions are also included.

Change point detection for clustered expression data

BackgroundTo detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.ResultsIn this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study.ConclusionSimultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.

A Non-Parametric Approach for Change-Point Detection of Multi-Parameters in Time-Series Data

Change-point analysis of time-series data plays a vital role in various fields of earth sciences under changing environments. Most of the analysis approaches were usually designed to detect the change-point in the level of time-series mean. In this study, we aimed to propose a non-parametric approach to detect the change-point of different parameters of time-series data. In this approach, the Boot- strap method, coupling with Kernel density estimation, was first used to estimate the probability distribution function (pdf) of a parameter before and after any potential change-points. Second, the Ar-index based on the uncross area of the two pdfs was designed to quantify the difference of the parameter before and after each potential change-point. Finally, the potential change-point owning the largest Ar-index value was determined as the locations of the change-point of the parameter. The hydrological extreme series from four stations in the Hanjiang basin were used to demonstrate this approach. The Pettitt test method commonly used in hydrology was employed as a comparison to indirectly analyze the reliability of the proposed approach. The results show that change-point detected by the proposed approach in the four stations are identified with those detected by the Pettitt approach in the level of time-series mean. But in comparison with the Pettitt test, the proposed approach can provide more detection information for other parameters, such as coefficient of variation (Cv) and coefficient of skewness (Cs) of the series. The results also show that the degree of change in the series mean is greater than its Cv and Cs, while the degree of change in series Cv is greater than its Cs.

A novel RSW&TST framework of MCPs detection for abnormal pattern recognition on large-scale time series and pathological signals in epilepsy.

To quickly and efficiently recognize abnormal patterns from large-scale time series and pathological signals in epilepsy, this paper presents here a preliminary RSW&TST framework for Multiple Change-Points (MCPs) detection based on the Random Slide Window (RSW) and Trigeminal Search Tree (TST) methods. To avoid the remaining local optima, the proposed framework applies a random strategy for selecting the size of each slide window from a predefined collection, in terms of data feature and experimental knowledge. For each data segment to be diagnosed in a current slide window, an optimal path towards a potential change point is detected by TST methods from the top root to leaf nodes with O(log3(N)). Then, the resulting MCPs vector is assembled by means of TST-based single CP detection on data segments within each of the slide windows. In our experiments, the RSW&TST framework was tested by using large-scale synthetic time series, and then its performance was evaluated by comparing it with existing binary search tree (BST), Kolmogorov-Smirnov (KS)-statistics, and T-test under the fixed slide window (FSW) approach, as well as the integrated method of wild binary segmentation and CUSUM test (WBS&CUSUM). The simulation results indicate that our RSW&TST is both more efficient and effective, with a higher hit rate, shorter computing time, and lower missed, error and redundancy rates. When the proposed RSW&TST framework is executed for MCPs detection on pathological ECG (electrocardiogram)/EEG (electroencephalogram) recordings of people in epileptic states, the abnormal patterns are roughly recognized in terms of the number and position of the resultant MCPs. Furthermore, the severity of epilepsy is roughly analyzed based on the strength and period of signal fluctuations among multiple change points in the stage of a sudden epileptic attack. The purpose of our RSW&TST framework is to provide an encouraging platform for abnormal pattern recognition through MCPs detection on large-scale time series quickly and efficiently.

Monitoring for a change point in a sequence of distributions

We propose a method for the detection of a change point in a sequence {Fi} of distributions, which are available through a large number of observations at each i≥1. Under the null hypothesis, the distributions Fi are equal. Under the alternative hypothesis, there is a change point i∗>1, such that Fi=G for i≥i∗ and some unknown distribution G, which is not equal to F1. The change point, if it exists, is unknown, and the distributions before and after the potential change point are unknown. The decision about the existence of a change point is made sequentially, as new data arrive. At each time i, the count of observations, N, can increase to infinity. The detection procedure is based on a weighted version of the Wasserstein distance. Its asymptotic and finite sample validity is established. Its performance is illustrated by an application to returns on stocks in the S&P 500 index.

After-hours esophagectomy may pose additional risk to patients with esophageal cancer.

BackgroundThe increase in the incidence of esophageal cancers (ECs) combined with fewer surgeons working at large centers will increase the likelihood of surgery for ECs being performed during later hours. This study aimed to compare esophagectomies’ operative outcomes for EC performed at different surgical starting times.MethodsThis was a single-center, retrospective study. Risk-adjusted cumulative sum curve analysis and Cox regression analysis were used to identify the potential change-point of surgical starting times. The participants were then divided into 2 groups according to the change-point time. Propensity score matching was used to control confounding factors between the 2 groups. We compared the short- and long-term outcomes in both groups.ResultsA total of 702 patients who underwent potentially radical esophagectomy from 7 May 2014 to 31 December 2017 in our institute were included. The 3-year all-cause mortality showed a significant change-point at 16:42, with an increment from 56.5% to 76.9% (P=0.043). Esophagectomy that commenced between 17:00–18:59 was associated with significantly lower overall survival (OS) [multivariate hazard ratio (HR): 2.47; 95% confidence interval (CI): 1.25 to 4.90; P=0.010] and disease-free survival (DFS) (multivariate HR: 2.14; 95% CI: 1.08 to 4.21; P=0.028). The participants were allocated to the during-hours group and the after-hours group according to the change-point of 17:00. A total of 84 participants in the during-hours group were matched to 33 participants in the after-hours group. The median operative time was shorter in the after-hours group [309 (during-hours) vs. 239 (after-hours) minutes, P=0.014); the after-hours group had a greater incidence of respiratory complications (22.63% vs. 45.45%, P=0.023) and 90-day mortality (0 vs. 9.09%, P=0.021). The 5-year OS (P=0.042) and DFS (P=0.030) were significantly higher in the during-hours group.ConclusionsEsophagectomies started during after-hours are correlated with poorer surgical outcomes. It is recommended to cancel selective esophagectomies due to commence after 17:00.

Comprehensive analysis on the evolution characteristics and causes of river runoff and sediment load in a mountainous basin of China’s subtropical plateau

Under the background of climate variability and anthropogenic stresses, changes of the runoff and sediment processes have drawn increasing concerns during the past decades. Analyzing the evolution characteristics and causes of runoff and sediment load is critical for the sustainable development of water resource and terrestrial ecosystems of river basins. This study presents an integrated approach for analyzing the evolution characteristics of the annual runoff and sediment load, including the trend, periodicity, change point and their correlations in the Wujiang River basin (WRB) –a typical mountainous river system in southwest China, based on the observed data during the period 1970–2016. On this basis, the contributions of climate variability and human activities on runoff change were first distinguished by using a widely applied Budyko-based hydrological sensitivity method, and then the quantified result was further applied in the attribution analysis of river sediment load change. Research results revealed that the change patterns of runoff and sediment processes, such as the trend, periodicity and potential change point, as well as the relationships of precipitation-runoff and runoff-sediment load are quite different in the WRB. Quantitative assessment revealed that climate variability contributes to 71.5%–85.6% changes of mean annual runoff in 1985–1994, 1995–2004 and 2005–2016 with reference to the baseline period of 1970–1984. In contrary, human activities dominate the reduction of sediment load in the river, and the contribution rate ranged in 67.2%–107.5%. Variations of annual runoff in the WRB was the direct result of climate variability, while land use change played a secondary role. The construction of cascade hydropower reservoirs, particularly in the upper and middle reaches of the Wujiang River is fundamentally responsible for the significant decreasing and the weakened periodicity of sediment load as well as the changed runoff-sediment load correlation at Wulong station in recent years. However, this influence was not big enough to modify the regime of annual runoff.

Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.

A changepoint analysis of spatio-temporal point processes

This work introduces a Bayesian approach to detecting multiple unknown changepoints over time in the inhomogeneous intensity of a spatio-temporal point process with spatial and temporal dependence within segments. We propose a new method for detecting changes by fitting a spatio-temporal log-Gaussian Cox process model using the computational efficiency and flexibility of integrated nested Laplace approximation, and by studying the posterior distribution of the potential changepoint positions. In this paper, the context of the problem and the research questions are introduced, then the methodology is presented and discussed in detail. A simulation study assesses the validity and properties of the proposed methods. Lastly, questions are addressed concerning potential unknown changepoints in the intensity of radioactive particles found on Sandside beach, Dounreay, Scotland.

Change-point cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients

Previous research on prostate cancer survival trends in the United States National Cancer Institute's Surveillance Epidemiology and End Results database has indicated a potential change-point in the age of diagnosis of prostate cancer around age 50. Identifying a change-point value in prostate cancer survival and cure could have important policy and health care management implications. Statistical analysis of this data has to address two complicating features: (1) change-point models are not smooth functions and so present computational and theoretical difficulties; and (2) models for prostate cancer survival need to account for the fact that many men diagnosed with prostate cancer can be effectively cured of their disease with early treatment. We develop a cure survival model that allows for change-point effects in covariates to investigate a potential change-point in the age of diagnosis of prostate cancer. Our results do not indicate that age under 50 is associated with increased hazard of death from prostate cancer.

Off-Line Detection of Multiple Change Points by the Filtered Derivative with p-Value Method

This article deals with off-line detection of change points, for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis method with linear time and memory complexity. Our method is based, on a filtered derivative method that detects the right change points as well as false ones. We improve the filtered derivative method by adding a second step in which we compute the p-values associated to every single potential change point. Then, we eliminate false alarms; that is, the change points that have p-values smaller than a given critical level. Next, we apply our method and penalized least squares criterion procedure to detect change points on simulated data sets and then we compare them. Eventually, we apply the filtered derivative with p-value method to the segmentation of heartbeat time series, and the detection of change points in the average daily volume of financial time series.

A change-point estimator using local Fourier series

In this paper, we propose a change-point estimator based on local Fourier series estimates. At each potential change point, two Fourier series estimates are computed, one using data to the left of the possible change point and the other using data to the right. The difference between these estimates is computed, and the change-point estimate is defined to be the point at which this absolute difference is maximised. Mean-squared error properties of our local series estimates are derived and the change-point estimator is shown to converege to the truth at rate n −1 (where n is the sample size). In a simulation study, the proposed change-point estimator has a better overall performance than a local linear method. A data-driven bandwidth selector is also proposed and applied to the classical Nile River data.

Hydrogen-induced structural change in Ni 90Al 10 metallic glass

Ni 90Al 10 amorphous membranes were prepared by means of molecular dynamics (MD) simulation technique with the aim of investigating the structural evolution induced by hydrogen with respect to hydrogen concentration. The short-range ordered (SRO) structures of the as-cast model sample and the structural change by hydrogen charging were analyzed using Voronoi tessellation method. This study indicates that prism and prism-like structures with low coordination number are generated, while intrinsically stable SRO structures in the amorphous membrane annihilate upon hydrogen charging. We discussed the relationship between these local atomic changes and the structural stability which was evaluated from the potential energy change point of view.

A Two-step Criterion Algorithm of Speaker Segmentation

为了提高说话人分割(SS)准确率，该文综合考虑了静音信息和性别信息在SS中的作用，提出了一种两步判决的SS算法。在从音频流中分离出语音段的基础上，采用两步判决的方法进行SS。第1步采用基频信息为主、性别模型为辅的策略进行SS，将相邻说话人基频差异大的说话人改变检测出来；第2步采用基于性别的改进T2判决公式进行SS，实现相邻说话人基频差异小的同性别SS，为此，该文提出了一个基于块的潜在说话人改变点检测算法。实验结果表明，本文算法提高了分割准确率，F1度量值可达85.14%。对于短时长(2 s)语音段的SS，该算法和传统的贝叶斯信息判决算法相比，漏检率减少了16%。

Changepoint detection in SPI transition probabilities

The Standardized Precipitation Index (SPI) has become a popular measure of drought across the globe. Several techniques have been employed in attempts to estimate the time at which any changes in drought patterns occur using categories defined by SPI values. Log-linear models, odds ratios and multiple confidence intervals have all been used while trying to estimate such changepoints and have met with varying degrees of success, in part due to small cell counts and the large number of inferences that must be carried out. The purpose of this paper is to provide an alternative method for the detection of a changepoint in SPI data, specifically in the matrix of transitions from one drought class to another. A resampling algorithm is provided that both detects a potential changepoint and determines its significance under the assumption that at most one changepoint is present. Simulation results are provided to validate the method as well as the suggested metrics for detection. Data from Kenya is used to provide four examples where the technique is implemented on observed rainfall data.

Method of real-time speaker change point detection, speaker tracking, and speaker model construction

A method is provided for real-time speaker change detection and speaker tracking in a speech signal. The method is a “coarse-to-refine” process, which consists of two stages: pre-segmentation and refinement. In the pre-segmentation process, the covariance of a feature vector of each segment of speech is built initially. A distance is determined based on the covariance of the current segment and a previous segment; and the distance is used to determine if there is a potential speaker change between these two segments. If there is no speaker change, the model of current identified speaker model is updated by incorporating data of the current segment. Otherwise, if there is a speaker change, a refinement process is utilized to confirm the potential speaker change point.

Asymptotic theory for bent-cable regression—the basic case

We use what we call the bent-cable model to describe potential change-point phenomena. The class of bent cables includes the commonly used broken stick (a bent cable without a bend segment). Theory for least-squares (LS) estimation is developed for the basic bent cable, whose incoming and outgoing linear phases have slopes 0 and 1, respectively, and are joined smoothly by a quadratic bend. Conditions on the design are given to ensure regularity of the estimation problem, despite non-differentiability of the model's first partial derivatives (with respect to the covariate and model parameters). Under such conditions, we show that the LS estimators (i) are consistent, regardless of a zero or positive true bend width; and (ii) asymptotically follow a bivariate normal distribution, if the underlying cable has all three segments. In the latter case, we show that the deviance statistic has an asymptotic chi-squared distribution with two degrees of freedom.