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We consider the problem of feature detection in the presence of clutter in spatial point processes. Classification methods have been developed in previous studies. Among these, Byers and Raftery (J Am Stat Assoc 93(442):577–584, 1998) models the observed Kth nearest neighbour distances as a mixture distribution and classifies the clutter and feature points consequently. In this paper, we enhance such approach in two manners. First, we propose an automatic procedure for selecting the number of nearest neighbours to consider in the classification method by means of segmented regression models. Secondly, with the aim of applying the procedure multiple times to get a "better" end result, we propose a stopping criterion that minimizes the overall entropy measure of cluster separation between clutter and feature points. The proposed procedures are suitable for a feature with clutter as two superimposed Poisson processes on any space, including linear networks. We present simulations and two case studies of environmental data to illustrate the method. [ABSTRACT FROM AUTHOR]

We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The procedure works by performing likelihood ratio tests at each time point, and two thresholds are chosen to filter unimportant test statistics and make decisions based on the aggregated test statistics respectively. We provide asymptotic theory concerning consistency and the asymptotic distribution if there are no changes. Simulation results suggest that our method can achieve similar performance to the idealised setting, where we have no constraints on communication between sensors, but substantially reduce the transmission costs. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008–2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75. [ABSTRACT FROM AUTHOR]

In data sequences measured over space or time, an important problem is accurate detection of abrupt changes. In partially labeled data, it is important to correctly predict presence/absence of changes in positive/negative labeled regions, in both the train and test sets. One existing dynamic programming algorithm is designed for prediction in unlabeled test regions (and ignores the labels in the train set); another is for accurate fitting of train labels (but does not predict changepoints in unlabeled test regions). We resolve these issues by proposing a new optimal changepoint detection model that is guaranteed to fit the labels in the train data, and can also provide predictions of unlabeled changepoints in test data. We propose a new dynamic programming algorithm, Labeled Optimal Partitioning, and we provide a formal proof that it solves the resulting non-convex optimization problem. We provide theoretical and empirical analysis of the time complexity of our algorithm, in terms of the number of labels and the size of the data sequence to segment. Finally, we provide empirical evidence that our algorithm is more accurate than the existing baselines, in terms of train and test label error. [ABSTRACT FROM AUTHOR]

Different environmental variables are often monitored using different sampling rates; examples include half‐hourly weather station measurements, daily CO2$$ {\mathrm{CO}}_2 $$ data, and six‐day satellite data. Further when researchers want to combine the data into a single analysis this often requires data aggregation or down‐scaling. When one is seeking to identify changes within multivariate data, the aggregation and/or down‐scaling processes obscure the changes we seek. In this article, we propose a novel changepoint detection algorithm which can analyze multiple time series for co‐occurring changepoints with potentially different sampling rates, without requiring preprocessing to a standard sampling scale. We demonstrate the algorithm on synthetic data before providing an example identifying simultaneous changes in multiple variables at a location on the Greenland ice sheet using synthetic aperture radar and weather station data. [ABSTRACT FROM AUTHOR]

Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of windows, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different changes in mean scenarios, and demonstrate its practical utility through its state-of-the-art performance at detecting anomalous behaviour in computer server data. [ABSTRACT FROM AUTHOR]

While there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or where the mean fluctuates locally between the abrupt changes that one wishes to detect. In these cases, default implementations, which are often based on assumptions of a constant mean between changes and independent noise, can lead to substantial over-estimation of the number of changes. We propose a principled approach to detect such abrupt changes that models local fluctuations as a random walk process and autocorrelated noise via an AR(1) process. We then estimate the number and location of changepoints by minimizing a penalized cost based on this model. We develop a novel and efficient dynamic programming algorithm, DeCAFS, that can solve this minimization problem; despite the additional challenge of dependence across segments, due to the autocorrelated noise, which makes existing algorithms inapplicable. Theory and empirical results show that our approach has greater power at detecting abrupt changes than existing approaches. We apply our method to measuring gene expression levels in bacteria. for this article are available online. [ABSTRACT FROM AUTHOR]

In regression models, predictor variables with inherent ordering, such ECOG performance status or novel biomarker expression levels, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to consider both dichotomous and linear forms for the variable. This allows for simultaneous assessment of the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. This method is applicable to continuous, binary, and survival outcomes, and it is easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation. [ABSTRACT FROM AUTHOR]

• Prophet Model provides understanding of the number of people affected daily by this disease. • The imposed interventions are reflected by five significant changepoints detected by the model in the growth curve of India. • The model forecasts90 days future growth trend and finds the peak time for all the six countries and six states of India. • The model has achieved around 85% MAPE for all the six countries and the six states of India. Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure. [Display omitted] [ABSTRACT FROM AUTHOR]

Sea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long‐term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument changes, location changes, earthquakes, land reclamation, dredging, etc., the sea level data could contain inhomogeneous shifts in their means, which can substantially impact trend estimates if ignored. To rigorously quantify the long‐term linear trends and return levels for the monthly maximum sea level data, we use a genetic algorithm to estimate the number and times of changepoints in the data. As strong periodicity and temporal correlation are pertinent to the data, bootstrap techniques are used to obtain more realistic confidence intervals to the estimated trends and return levels. We find that the consideration of changepoints changed the estimated linear trends of 89 tide gauges (approximately 30% of tide gauges considered) by more than 20cmcentury‐1. Our results are summarized in maps with estimated extreme sea level trends and 50‐year return levels. [ABSTRACT FROM AUTHOR]

Temporal discontinuities in time series represent one of the classic problems of time series. Such discontinuities are often analyzed by detecting changes at specific times in the parameters governing a regression model fit to the series. The regression framework examined here contains three classes of predictors: functional form, seasonal, and stochastic. Regression errors are allowed to observe a general stationary structure. Methods are proposed that provide the analyst with full exibility in selecting which set of regression parameters are allowed to change under the alternative hypothesis. Here, we also examine several mathematical complications that arise in the development of such procedures. A simulation study illustrates the effcacy of the proposed methodology, where a test statistic based on the residuals from an ARMA model is shown to perform most favorably. The methods are applied to a carbon dioxide time series measured at Mauna Loa Observatory, where a shift in the seasonal variations is detected (in addition to a known shift in trend), and to a series of monthly temperatures at Barrow, Alaska, where only a shift in trend is found. [ABSTRACT FROM AUTHOR]

Rabindranath Tagore (1861-1941) was a world famous Nobel laureate poet, philosopher, educationist and one of the most prolific song writers and composers in India's history. He was the first Nobel laureate of Asian origin who received the Nobel prize in literature in 1913. Among his numerous manifestations of innovation, creation and excellence – his songs were very special to him. He wrote and composed songs since he was only eleven years and continued till the end of his day. Among the many facets of his creative works, the composition of his about 2200 songs stand out in sheer number, artistic caliber, scholarly appreciation and social impact. In this paper we focus on his productivity of songs by year, in thematic as well as non-thematic categorizations. We may visualize the total number of annual song-counts as a time series X(t) where t denotes age or year. We summarize and graphically study this time-series as well as nonparametrically study its changepoints. We observe the upper and lower 5% quantile counts and associate the years that relate to extremely 'low' or 'high' productivity. We also associate the changepoints with major events (turning points) of his life. We also view the multivariate time series by the themes or categories – namely 'Puja' (Devotion), 'Prem' (Love), 'Prakriti' (Nature), 'Swadesh' (Motherland) and 'Vichitra' (Medley). Thematic categorizations then give rise to a multivariate time series We summarize and graphically describe the time-series, focusing on the upper and lower extremes of these counts, as well as the corresponding central tendencies, skewness, shapes, temporal patterns and variations as well as changepoints. We observe that the high extremal counts are always associated with a handful of distinct, extreme and unexpected events in Tagore's life. We further study changepoints and observe that these are associated with either a handful of distinct, extreme events or some turning-points in Tagore's life. To our knowledge Tagore's song-counts have never before been studied as a time-series and hence the insight we find here is novel to both statisticians as well as Tagore-enthusiasts. [ABSTRACT FROM AUTHOR]

Objective: Several models have been proposed for the evolution of Alzheimer’s disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD.Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset.Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10–15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset).Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.

Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.

Objective: Several models have been proposed for the evolution of Alzheimer's disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD. Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset. Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10–15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset). Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance. [ABSTRACT FROM AUTHOR]

Detecting recent changepoints in time-series can be important for short-term prediction, as we can then base predictions just on the data since the changepoint. In many applications, we have panel data, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such panel data that aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computationally efficient as it involves analysing each time-series independently to obtain a profile-likelihood like quantity that summarizes the evidence for the series having either no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series that has their most recent changepoint at that time. We demonstrate the usefulness of this method on two datasets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms. [ABSTRACT FROM AUTHOR]

Joinpoint regression models are popular in various situations (modeling different trends in economics, mortality and incidence series or epidemiology studies and clinical trials). The literature on joinpoint regression mostly focuses on either the frequentist point of view, or discusses Bayesian approaches instead. A model selection step in all these scenarios considers only some limited set of alternatives, from which the final model is chosen. We present a different model estimation approach: the final model is selected out of all possible alternatives admitted by the data. We apply the $$L_{1}$$ -regularization idea and via the sparsity principle we identify significant joinpoint locations to construct the final model. Some theoretical results and practical examples are given as well. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic implications of making concessions to terrorist kidnappers. We apply a Bayesian Poisson changepoint model to kidnapping incidents associated with three cohorts of countries that differ in their frequency of granting concessions. Depending on the cohort of countries during 2001–2013, terrorist negotiation successes encouraged 64% to 87% more kidnappings. Our findings also hold for 1978–2013, during which these negotiation successes encouraged 26% to 57% more kidnappings. Deterrent aspects of terrorist casualties are also quantified; the dominance of religious fundamentalist terrorists meant that such casualties generally did not curb kidnappings. [ABSTRACT FROM AUTHOR]

A representative data set is used to provide an example comparing classical and Bayesian approaches to making inferences about the point in a sequence of random variables at which the underlying distribution may shift. Inferences about the underlying distributions themselves are also made. Most of the underlying R code used in the analysis is shown in the appendix. [ABSTRACT FROM AUTHOR]

Circular data – data whose values lie in the interval [0,2π) – are important in a number of application areas. In some, there is a suspicion that a sequence of circular readings may contain two or more segments following different models. An analysis may then seek to decide whether there are multiple segments, and if so, to estimate the changepoints separating them. This paper presents an optimal method for segmenting sequences of data following the von Mises distribution. It is shown by example that the method is also successful in data following a distribution with much heavier tails. [ABSTRACT FROM AUTHOR]

Objectives: One challenge that arises when analyzing mobile health (mHealth) data is that updates to the proprietary algorithms that process these data can change apparent patterns. Since the timings of these updates are not publicized, an analytic approach is necessary to determine whether changes in mHealth data are due to lifestyle behaviors or algorithmic updates. Existing methods for identifying changepoints do not consider multiple types of changepoints, may require prespecifying the number of changepoints, and often involve nonintuitive parameters. We propose a novel approach, Automated Selection of Changepoints using Empirical P -values and Trimming (ASCEPT), to select an optimal set of changepoints in mHealth data., Materials and Methods: ASCEPT involves 2 stages: (1) identification of a statistically significant set of changepoints from sequential iterations of a changepoint detection algorithm; and (2) trimming changepoints within linear and seasonal trends. ASCEPT is available at https://github.com/matthewquinn1/changepointSelect., Results: We demonstrate ASCEPT's utility using real-world mHealth data collected through the Precision VISSTA study. We also demonstrate that ASCEPT outperforms a comparable method, circular binary segmentation, and illustrate the impact when adjusting for changepoints in downstream analysis., Discussion: ASCEPT offers a practical approach for identifying changepoints in mHealth data that result from algorithmic updates. ASCEPT's only required parameters are a significance level and goodness-of-fit threshold, offering a more intuitive option compared to other approaches., Conclusion: ASCEPT provides an intuitive and useful way to identify which changepoints in mHealth data are likely the result of updates to the underlying algorithms that process the data., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

This paper introduces a method to identify an undocumented changepoint time in a daily precipitation series. A two-state Markov chain is used to induce dependence in the precipitation amounts; our dynamics allow for seasonality in the daily observations, a structure inherent to many nonequatorial region series. No current precipitation changepoint techniques exist that consider day-to-day dependencies, the zero support set aspect (the fact that most measurements are zero), and the periodic dynamics of the problem. The test statistic is constructed by applying cumulative sum methods to a strategically devised set of one-step-ahead prediction residuals. The methods are robust to distributional assumptions, requiring only seasonal mean and transition probability estimators. Simulations are presented that demonstrate the efficacy of the methods; application to two daily precipitation series is made. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We propose a segmented discrete-time model for the analysis of event history data in demographic research. Through a unified regression framework, the model provides estimates of the effects of explanatory variables and jointly accommodates flexibly non-proportional differences via segmented relationships. The main appeal relies on ready availability of parameters, changepoints, and slopes, which may provide meaningful and intuitive information on the topic. Furthermore, specific linear constraints on the slopes may also be set to investigate particular patterns. We investigate the intervals between cohabitation and first childbirth and from first to second childbirth using individual data for Italian women from the Second National Survey on Fertility. The model provides insights into dramatic decrease of fertility experienced in Italy, in that it detects a 'common' tendency in delaying the onset of childbearing for the more recent cohorts and a 'specific' postponement strictly depending on the educational level and age at cohabitation. [ABSTRACT FROM AUTHOR]

Annually averaged daily maximum and minimum surface temperatures from southeastern China were evaluated for artificial discontinuities using three different tests for undocumented changepoints. Changepoints in the time series were identified by comparing each target series to a reference calculated from values observed at a number of nearby stations. Under the assumption that no trend was present in the sequence of target-reference temperature differences, a changepoint was assigned to the target series when at least two of the three tests rejected the null hypothesis of no changepoint at approximately the same position in the difference series. Each target series then was adjusted using a procedure that accounts for discontinuities in average temperature values from nearby stations that otherwise could bias estimates of the magnitude of the target series step change. A spatial comparison of linear temperature trends in the adjusted annual temperature series suggests that major relative discontinuities were removed in the homogenization process. A greater number of relative change points were detected in annual average minimum than in average maximum temperature series. Some evidence is presented which suggests that minimum surface temperature fields may be more sensitive to changes in measurement practice than maximum temperature fields. In addition, given previous evidence of urban heat island (i.e., local) trends in this region, the assumption of no slope in a target-reference difference series is likely to be violated more frequently in minimum than in maximum temperature series. Consequently, there may be greater potential to confound trend and step changes in minimum temperature series. [ABSTRACT FROM AUTHOR]

When an individual player or team enjoys periods of good form, and when these occur, is a widely observed phenomenon typically called‘streakiness’. It is interesting to assess which team is a streaky team, or who is a streaky player in sports. Such competitors might have a large number of successes during some periods and few or no successes during other periods. Thus, their success rate is not constant over time. We provide a Bayesian binary segmentation procedure for locating changepoints and the associated success rates simultaneously for these competitors. The procedure is based on a series of nested hypothesis tests each using the Bayes factor or the Bayesian information criterion. At each stage, we only need to compare a model with one changepoint with a model based on a constant success rate. Thus, the method circumvents the computational complexity that we would normally face in problems with an unknown number of changepoints. We apply the procedure to data corresponding to sports teams and players from basketball, golf and baseball. [ABSTRACT FROM AUTHOR]

Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference. [ABSTRACT FROM AUTHOR]

We develop a Bayesian multiple changepoint model to infer spatial phylogenetic variation (SPV) along aligned molecular sequence data. SPV occurs in sequences from organisms that have undergone biological recombination or when evolutionary rates and selective pressures vary along the sequences. This Bayesian approach permits estimation of uncertainty regarding recombination, the crossing-over locations, and all other model parameters. The model assumes that the sites along the data separate into an unknown number of contiguous segments, each with possibly different evolutionary relationships between organisms, evolutionary rates, and transition: transversion ratios. We develop a transition kernel, use reversible-jump Markov chain Monte Carlo to fit our model, and draw inference from both simulated and real data. Through simulation, we examine the minimal length recombinant segment that our model can detect for several levels of evolutionary divergence. We examine the entire genome of a reported human immunodeficiency virus (HIV)-1 isolate, related to a purported recombinant virus thought to be the causative agent of an epidemic outbreak of HIV-1 infection among intravenous drug users in Russia. We find that regions of the genome differ in their evolutionary history and selective pressures. There is strong evidence for multiple crossovers along the genome and frequent shifts in selective pressure changes throughout the vif through env genes. [ABSTRACT FROM AUTHOR]

This article studies the problem of testing and locating changepoints in likelihood ratios of two multinomial probability vectors. We propose a binary search procedure to detect the changepoints in the sequence of the ratios of probabilities and obtain the maximum likelihood estimators of two multinomial probability vectors under the assumption that the probability ratio sequence has a changepoint. We also give a strongly consistent estimator for the changepoint location. An information theoretic approach is used to test the equality of two discrete probability distributions against the alternative that their ratios have a changepoint. Approximate critical values of the test statistics are provided by simulation for several choices of model parameters. Finally, we examine a real life data set pertaining to average daily insulin dose from the Boston Collaborative Drug Surveillance Program and locate the changepoints in the probability ratios. [ABSTRACT FROM AUTHOR]

• A new hydrological regions delineation method based on changepoint analysis is proposed. • Similarity between regions is based on the location of changepoints in time series. • The method allows the estimation of local and regional changing linear trends. • The method was applied to 11 precipitation series in the Tensift basin, Morocco. • Cross-wavelet analysis is used to confirm the homogeneity of the delineated regions. Hydrologic regionalization consists of regrouping stations and catchments in pools based on a similarity measure. Regionalization is commonly used to extract a robust signal that can be used to describe the hydrology of the region or extrapolated to a location without measured information. Obviously, the similarity measure used affects the type of hydrological behavior one would expect from stations within a region. Most regionalization methods assume a stable and/or linear relationship between parameters of interests while it is well known that the physical processes driving the behavior of hydrometeorological variables are inherently non-linear and non-stationary. In this paper, we propose a similarity measure that is based on the location of changepoints in hydrological time series. The proposed method has the unique advantage over other hydrological region delineation methods to detect regions where hydrological member stations are non-linearly correlated, and where the strength of the relation varies with time. It therefore has the potential to uncover similarities that would not have been detected by existing regionalization techniques. The proposed method is applied to the Tensift watershed located in Morocco, North Africa. The coherence of the detected regions is checked using wavelet coherence. [ABSTRACT FROM AUTHOR]

In this paper we study a fully parametric proportional hazards model in which the covariates as well as the parameters of the base distribution can vary with time. We consider the application of this model to the case of extreme value base-line distribution with rightcensored data and obtain the equations for the estimate by maximum likelihood from the model parameters. [ABSTRACT FROM AUTHOR]

Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America. [ABSTRACT FROM AUTHOR]

Preeclampsia (PrE) is a leading complication of pregnancy characterized by vascular dysfunction. Characterizing the longitudinal changes in vascular function prior to PrE onset is critical to the identification of optimal timepoints for vascular assessment and the development of effective early screening strategies. In this prospective longitudinal study of women with singleton high-risk pregnancies, arterial stiffness and wave reflection parameters were assessed using applanation tonometry at 10–13 weeks' gestation and repeated every 4 weeks throughout pregnancy. Changepoints in carotid-femoral pulse wave velocity (cfPWV), carotid-radial PWV (crPWV), augmentation index (AIx), time to wave reflection (T 1R), pulse pressure amplification (PPA), and subendocardial viability ratio (SEVR) were compared between women who did and did not subsequently develop PrE. A changepoint in cfPWV and crPWV was detected at 14–17 weeks' gestation. cfPWV then increased in women who went on to develop PrE but decreased in women who did not; a 1.2 m/s difference in cfPWV between the groups was observed at 22–25 weeks' gestation. Conversely, crPWV converged in the two groups from a baseline difference of 1.05 m/s (95% credible interval: 0.37, 1.72). Women who subsequently developed PrE demonstrated an increase in AIx at 18–21 weeks' gestation that was not seen in women who did not develop PrE until 30–33 weeks. No differences in T 1R , PPA, or SEVR were observed between the groups. Altered vascular adaptations were detected using measures of arterial stiffness and wave reflection in the early second trimester of pregnant women who developed PrE compared to those who did not. These findings demonstrate the potential clinical utility of arterial stiffness and wave reflection parameters as an early screening tool for PrE, which can be used to inform clinical management of high-risk pregnancies. • Altered vascular adaptations in women with PrE were noted early in the second trimester. • A changepoint in cfPWV was detected at 14–17 weeks. • cfPWV was higher by 1.2 m/s at 22–25 weeks in women who developed PrE. • AIx increased at 18–21 weeks in women with PrE, while at 30–33 weeks in those without. [ABSTRACT FROM AUTHOR]

Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications. • New fuzzy changepoint based evaluation method focussed on local scale temporal events. • The method factors in uncertainty of changepoint locations in evaluation of timing. • Provides framework to evaluate numerical model ability to capture fine scale events. [ABSTRACT FROM AUTHOR]