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We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with infinite-dimensional covariates than in regression models with scalar or vector-valued covariates due to a slower rate of convergence of the parameter estimators. Yet the suggested change point test is asymptotically distribution-free and consistent for one-change point alternatives. In the latter case we also show consistency of a change point estimator. [ABSTRACT FROM AUTHOR]

Abstract Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. Despite a shaky understanding of the backbone technology, the price mechanism, and the business model, investors’ risk appetites pushed crypto market values to record highs. In addition, pricings are largely based on the perception of the market, making crypto assets naturally embedded with extreme volatility. Perhaps unsurprisingly, the new asset class has become an integral part of the investor’s portfolio, which traditionally consists of stock, commodities, forex, or any type of derivative. Therefore, it is critical to unearth possible connections between crypto currencies and traditional asset classes, scrutinizing correlational upheavals. Numerous research studies have focused on connectedness issues among the stock market, commodities, or other traditional asset classes. Scant attention has been paid, however, to similar issues when cryptos join the mix. We fill this void by studying the connectedness of the two biggest crypto assets to the stock market, both in terms of returns and volatility, through the Diebold Francis spillover model. In addition, through a novel bidirectional algorithm that is gaining currency in statistical inference, we locate times around which the nature of such connectedness alters. Subsequently, using Hausdorff-type metrics on such estimated changes, we cluster spillover patterns to describe changes in the dependencies between which two assets are evidenced to correlate with those between which other two. Creating an induced network from the cluster, we highlight which specific dependencies function as crucial hubs, how the impacts of drastic changes such as COVID-19 ripple through the networks—the Rings of Fire—of spillover dependencies.

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov–Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results, which are compared to recent results and applied to two sets of real data. [ABSTRACT FROM AUTHOR]

Here, we propose combining empirical Bayes modeling with recent advances in Markov chain Monte Carlo filters for hidden Markov models. In doing so, we address long-standing problems in the reconstruction of 3D images, with uncertainty quantification, from noisy 2D pixels in cryogenic electron microscopy and other applications, such as brain network development in infants. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flood events classified for New Zealand according to their hydrograph shape. • Key drivers determining flood types: rainfall variability and antecedent conditions. • Small and steep catchments dominated by heavy rainfall floods. • Majority of catchments characterised by medium- to long-duration rainfall floods. A major source of uncertainty in flood statistics are the different flood generation processes. These make the assumption of homogeneous samples questionable. To overcome this issue, a framework for assessing the influence of catchment and climate attributes on flood-generating processes and their effect on flood statistics has been developed and applied to 252 catchments in New Zealand. Mean daily discharge data time series with a length ranging from 20 to 81 years were used. Flood events were classified according to their hydrograph shape. Three types were considered based on the different forcing: heavy rainfall of short duration (termed R1), moderate rainfall of medium intensity and duration (R2), and long-duration rainfall sequences of usually larger spatial extent (R3). The dominant flood type in each catchment was then linked to catchment and climate attributes. This allowed to identify the impact of each flood type on flood statistics and how the flood types have changed over time. The main drivers determining the flood type were rainfall variability and antecedent conditions. Small and steep catchments were dominated by heavy-rainfall floods of shorter duration, while flat and wet catchments were dominated by long-duration floods with larger volumes. Such information can support selection of effective flood protection and management measures. [ABSTRACT FROM AUTHOR]

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov–Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results, which are compared to recent results and applied to two sets of real data.

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We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments S at a given error level, which serves as a tuning parameter. For a proper choice of this parameter, this probability tends exponentially fast to one, as sample size increases. We further show that the location and size of segments are estimated at minimax optimal rate (compared to a Gaussian setting) up to a log-factor. Thereby, our approach leads to (asymptotically) uniform confidence bands for the entire quantile regression function in a fully nonparametric setup. The procedure is efficiently implemented using dynamic programming techniques with double heap structures, and software is provided. Simulations and data examples from genetic sequencing and ion channel recordings confirm the robustness of the proposed procedure, which at the same time reliably detects changes in quantiles from arbitrary distributions with precise statistical guarantees. for this article are available online. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L'Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Although the two algorithms lead to similar results in the simulated scenarios, the proposed algorithm results in greater flexibility and automation capacity, as shown in the real data analysis. Indeed, our proposed algorithm does not require testing and optimization phases, resulting potentially very useful in earthquakes routine analysis for novel seismic networks or in regions whose earthquakes characteristics are unknown. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the mean change-point models based on associated sequences. Under some weak conditions, we obtain a limit distribution of CUSUM statistic which can be used to judge the mean change-mount δ n is satisfied or dissatisfied n 1 / 2 δ n = o (1). We also study the consistency of sample covariances and change-point location statistics. Based on Normality and Lognormality data, some simulations such as empirical sizes, empirical powers and convergence are presented to test our results. As an important application, we use CUSUM statistics to do the mean change-point analysis for a financial series. [ABSTRACT FROM AUTHOR]

It is very important to study the occurrence of high levels of particulate matter due to the potential harm to people's health and to the environment. In the present work we use a non-homogeneous Poisson model to analyse the rate of exceedances of particulate matter with diameter smaller that 2.5 microns (PM 2.5 ). Models with and without change-points are considered and they are applied to data from Bogota, Colombia, and Mexico City, Mexico. Results show that whereas in Bogota larger particles pose a more serious problem, in Mexico City, even though nowadays levels are more controlled, in the recent past PM 2.5 were the ones causing serious problems. [ABSTRACT FROM AUTHOR]

Abstract Background The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them. Results We introduce a novel method, HiCKey, to decipher hierarchical TAD structures in Hi-C data and compare them across samples. We first derive a generalized likelihood-ratio (GLR) test for detecting change-points in an interaction matrix that follows a negative binomial distribution or general mixture distribution. We then employ several optimal search strategies to decipher hierarchical TADs with p values calculated by the GLR test. Large-scale validations of simulation data show that HiCKey has good precision in recalling known TADs and is robust against random collisions of chromatin interactions. By applying HiCKey to Hi-C data of seven human cell lines, we identified multiple layers of TAD organization among them, but the vast majority had no more than four layers. In particular, we found that TAD boundaries are significantly enriched in active chromosomal regions compared to repressed regions. Conclusions HiCKey is optimized for processing large matrices constructed from high-resolution Hi-C experiments. The method and theoretical result of the GLR test provide a general framework for significance testing of similar experimental chromatin interaction data that may not fully follow negative binomial distributions but rather more general mixture distributions.

Motivated by real‐world data of monthly values of precipitation, minimum, and maximum temperature recorded at 360 monitoring stations covering the Italian territory for 60 years (12×60 months), in this work we propose a change‐point model for multiple multivariate time series, inspired by the hierarchical Dirichlet process. We assume that each station has its change‐point structure and, as main novelties, we allow unknown subsets of the parameters in the data likelihood to stay unchanged before and after a change‐point, that stations possibly share values of the same parameters and that the unknown number of weather regimes is estimated as a random quantity. Owing to the richness of the formalization, our proposal enables us to identify clusters of spatial units for each parameter, evaluate which parameters are more likely to change simultaneously, and distinguish between abrupt changes and smooth ones. The proposed model provides useful benchmarks to focus monitoring programs regarding ecosystem responses. Results are shown for the whole data, and a detailed description is given for three monitoring stations. Evidence of local behaviors includes highlighting differences in the potential vulnerability to climate change of the Mediterranean ecosystems from the Temperate ones and locating change trends distinguishing between continental plains and mountain ranges. [ABSTRACT FROM AUTHOR]

In this article, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long‐run US money demand function demonstrates the practical importance of this methodology. [ABSTRACT FROM AUTHOR]

To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet, temporal structure is everywhere in nature and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with "output noise" that corrupts the response-selection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans. [ABSTRACT FROM AUTHOR]

The area under the receiver operating characteristic curve (AUC) is one of the most popular measures for evaluating the performance of a predictive model. In nested models, the change in AUC (ΔAUC) can be a discriminatory measure of whether the newly added predictors provide significant improvement in terms of predictive accuracy. Recently, several authors have shown rigorously that ΔAUC can be degenerate and its asymptotic distribution is no longer normal when the reduced model is true, but it could be the distribution of a linear combination of some χ 12 random variables [ 1 , 2 ]. Hence, the normality assumption and existing variance estimate cannot be applied directly for developing a statistical test under the nested models. In this paper, we first provide a brief review on the use of ΔAUC for comparing nested logistic models and the difficulty of retrieving the reference distribution behind. Then, we present a special case of the nested logistic regression models that the newly added predictor to the reduced model contains a change-point in its effects. A new test statistic based on ΔAUC is proposed in this setting. A simple resampling scheme is proposed to approximate the critical values for the test statistic. The inference of the change-point parameter is done via m -out-of- n bootstrap. Large-scale simulation is conducted to evaluate the finite-sample performance of the ΔAUC test for the change-point model. The proposed method is applied to two real-life datasets for illustration. [ABSTRACT FROM AUTHOR]

Various statistical problems can be formulated in terms of a bilinear form of the covariance matrix. Examples are testing whether coordinates of a high‐dimensional random vector are uncorrelated, constructing confidence intervals for the risk of optimal portfolios or testing for the stability of a covariance matrix, especially for factor models. Extending previous works to a general high‐dimensional multivariate linear process framework and factor models, we establish distributional approximations for the associated bilinear form of the sample covariance matrix. These approximations hold for increasing dimension without any constraint relative to the sample size. The results are used to construct change‐point tests for the covariance structure, especially in order to check the stability of a high‐dimensional factor model. Tests based on the cumulated sum (CUSUM), self‐standardized CUSUM and the CUSUM statistic maximized over all subsamples are considered. Size and power of the proposed testing methodology are investigated by a simulation study and illustrated by analyzing the Fama and French factors for a change due to the SARS‐CoV‐2 pandemic. [ABSTRACT FROM AUTHOR]

Background: The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them. Results: We introduce a novel method, HiCKey, to decipher hierarchical TAD structures in Hi-C data and compare them across samples. We first derive a generalized likelihood-ratio (GLR) test for detecting change-points in an interaction matrix that follows a negative binomial distribution or general mixture distribution. We then employ several optimal search strategies to decipher hierarchical TADs with p values calculated by the GLR test. Large-scale validations of simulation data show that HiCKey has good precision in recalling known TADs and is robust against random collisions of chromatin interactions. By applying HiCKey to Hi-C data of seven human cell lines, we identified multiple layers of TAD organization among them, but the vast majority had no more than four layers. In particular, we found that TAD boundaries are significantly enriched in active chromosomal regions compared to repressed regions. Conclusions: HiCKey is optimized for processing large matrices constructed from high-resolution Hi-C experiments. The method and theoretical result of the GLR test provide a general framework for significance testing of similar experimental chromatin interaction data that may not fully follow negative binomial distributions but rather more general mixture distributions. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian quantile bent-cable growth model for modeling data with multi-phasic trajectories of response variables measured in longitudinal studies. Estimating and identifying such possible multiple phasic change points may be of substantial interest since it provides a general life-course view of the developmental trajectories and also when the directions of the trajectories are disrupted. For getting a complete picture of such developmental trajectories, quantile growth models, at different parts (quantiles) of the response variables, are better than the commonly used conditional mean models. For each quantile, we develop bent-cable models to assess multi-phasic patterns of trajectories of longitudinal HIV/AIDS data with left-censoring and skewness. The proposed procedures are illustrated using real data from an AIDS clinical study. [ABSTRACT FROM AUTHOR]

Abstract Background Pancreatic cancer is acquiring increasing prominence as a cause of cancer death in the population. The purpose of this study was to analyze long-term pancreatic cancer mortality trends in Spain and evaluate the independent effects of age, death period and birth cohort on these trends. Methods Population and mortality data for the period 1952–2012 were obtained from the Spanish National Statistics Institute. Pancreatic cancer deaths were identified using the International Classification of Diseases ICD-6 to ICD-9 (157 code) and ICD-10 (C25 code). Age-specific and age-adjusted mortality rates were computed by sex, region and five-year period. Changes in pancreatic cancer mortality trends were evaluated using joinpoint regression analyses by sex and region. Age-period-cohort log-linear models were fitted separately for each sex, and segmented regression models were used to detect changes in period- and cohort-effect curvatures. Results In men, rates increased by 4.1% per annum from 1975 until the mid-1980s and by 1.1% thereafter. In women, there was an increase of 3.6% per annum until the late 1980s, and 1.4% per annum from 1987 to 2012. With reference to the cohort effects, there was an increase in mortality until the generations born in the 1950s in men and a subsequent decline detected by the change point in 1960. A similar trend was observed in women, but the change point occurred 10 years later than in men. Conclusions Pancreatic cancer mortality increased over the study period in both sexes and all regions. An important rise in rates -around 4% annually- was registered until the 1980s, and upward trends were more moderate subsequently. The differences among sexes in trends in younger generations may be linked to different past prevalence of exposure to some risk factors, particularly tobacco, which underwent an earlier decrease in men than in women.

We apply a maximal likelihood ratio test for the presence of multiple change-points in the covariate effects based on the Cox regression model. The covariate effect is assumed to change smoothly at one or more unknown change-points. The number of change-points is inferred by a sequential approach. Confidence intervals for the regression and change-point parameters are constructed by a bootstrap method based on Bernstein polynomials conditionally on the number of change-points. The methods are assessed by simulations and are applied to two datasets. [ABSTRACT FROM AUTHOR]

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behavior in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces. for this article are available online. [ABSTRACT FROM AUTHOR]

It is commonly recognized that software development is highly unpredictable and software quality may not be easily enhanced after software product is finished. During the software development life cycle (SDLC), project managers have to solve many technical and management issues, such as high failure rate, cost over‐run, low quality, and late delivery. Consequently, in order to produce robust and reliable software product(s) on time and within budget, project managers and developers have to appropriately allocate limited time, manpower, development, and testing effort. In the past, the distribution of testing effort or manpower can typically be described by the Weibull or Rayleigh model. Practically, it should be noticed that development environments or methods could be changed due to some reasons. Thus, when we plan to perform software reliability modeling and prediction, these changes or variations occurring in the development process have to be taken into consideration. In this paper, we will study how to use the Parr‐curve model with multiple change‐points to depict the consumption of testing effort and how to perform further software reliability analysis. Some mathematical properties of proposed model will be given and discussed. The applicability and performance of our proposed model will be demonstrated and assessed through real software failure data. Experimental results are analyzed and compared with other existing models to show that our proposed model gives better predictions. Finally, an optimal software release policy based on cost‐reliability criteria is proposed and studied. The main purpose is aimed at minimizing the total cost of software development when a reliability objective is given. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in the energy exchange, water cycle, carbon cycle, biogeochemical cycle, and maintenance of surface ecosystems. In recent years, regional vegetation cover has changed significantly. This study used statistical analyses, including the Mann-Kendall trend test, the Hurst exponent, and Pettitt test, to analyze the characteristics of temporal and spatial variation of vegetation coverage in the Xijiang River basin from 2000 to 2013. The results showed that vegetation coverage of 98.76% of the Xijiang River basin is weakly variable (Cv < 0.1). The area with significantly increased vegetation accounts for 43.45% of the total area (p < = 0.05). A total of 19.47% of vegetation coverage in the Xijiang River basin had significant change-points from 2004 to 2008 (p < = 0.05), and the area of concave change-points accounted for 25.99% of the total area of point increased the vegetation coverage. At an altitude of 500–2000 m, the altitude has an inhibitory effect on vegetation coverage. When the slope is less than 35 degrees, the slope has a promoting effect on vegetation coverage. Rich precipitation resources are the main source of soil water supply, and higher temperature provides better thermal energy resources, which may have a significant impact on vegetation growth in the future and cause time lag effects of climatic factors on vegetation coverage. The vegetation coverage and the area affected by the precipitation and temperature (time lag factors) accounted for 32.99% and 31.47% of the total watershed, respectively. The correlation between climatic factors, topographic factors, and vegetation coverage increased over time. The results from this study will help to further deepen the understanding of vegetation cover and its influencing factors, and provide a scientific basis for ecological restoration projects such as vegetation restoration in the Xijiang River basin of China. [ABSTRACT FROM AUTHOR]

This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select the best model solution for examining if international spillovers come from multivariate volatility, time variation, or contemporaneous relationship. An empirical application among Central-Eastern and Western Europe economies is conducted to describe the performance of the methodology, with particular emphasis on the Great Recession and post-crisis periods. A simulated example is also addressed to highlight the performance of the estimating procedure. Findings from evidence-based forecasting are also addressed to evaluate the impact of an ongoing pandemic crisis on the global economy.

• Develop an empirical Bayesian information criterion (emBIC) for change-points detection. • Develop a Gibbs sampler induced stochastic search algorithm to find the minimiser of emBIC with probability 1. • Develop a 3-step change-points computing procedure, integrating emBIC, Gibbs sampler and post-selection calibration. Uncovering hidden change-points in an observed signal sequence is challenging both mathematically and computationally. We tackle this by developing an innovative methodology based on Markov chain Monte Carlo and statistical information theory. It consists of an empirical Bayesian information criterion (emBIC) to assess the fitness and virtue of candidate configurations of change-points, and a stochastic search algorithm induced from Gibbs sampling to find the optimal change-points configuration. Our emBIC is derived by treating the unknown change-point locations as latent data rather than parameters as is in traditional BIC, resulting in significant improvement over the latter which is known to mostly over-detect change-points. The use of the Gibbs sampler induced search enables one to quickly find the optimal change-points configuration with high probability and without going through computationally infeasible enumeration. We also integrate the Gibbs sampler induced search with a current BIC-based change-points sequential testing method, significantly improving the method's performance and computing feasibility. We further develop two comprehensive 3-step computing procedures to implement the proposed methodology for practical use. Finally, simulation studies and real examples analyzing business and genetic data are presented to illustrate and assess the procedures. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Bayesian approach for segmented Weibull distributions which could be a good alternative to analyze medical survival data in the presence of censored observations and covariates. With the obtained Bayesian estimated change-points we could get an excellent fit of the proposed model to any data sets. With the proposed methodology, it is also possible to identify survival times intervals where a covariate could have significantly different effects when compared to other lifetime intervals, an important point under a clinical view. The obtained Bayesian estimates are obtained using standard Markov Chain Monte Carlo methods. Some exam- ples with real data sets illustrate the proposed methodology and its potential clinical value. [ABSTRACT FROM AUTHOR]

A non-homogeneous Poisson process is used to study the rate at which a pollutant's concentration exceeds a given threshold of interest. An anisotropic spatial model is imposed on the parameters of the Poisson intensity function. The main contribution here is to allow the presence of change-points in time since the data may behave differently for different time frames in a given observational period. Additionally, spatial anisotropy is also imposed on the vector of change-points in order to account for the possible correlation between different sites. Estimation of the parameters of the model is performed using Bayesian inference via Markov chain Monte Carlo algorithms, in particular, Gibbs sampling and Metropolis-Hastings. The different versions of the model are applied to ozone data from the monitoring network of Mexico City, Mexico. An analysis of the results obtained is also given. [ABSTRACT FROM AUTHOR]

Change-point estimation has received much attention in the literature as it plays a significant role in several signal processing applications. However, the study of the optimal estimation performance in such context is a difficult task since the unknown parameter vector of interest may contain both continuous and discrete parameters, namely the parameters associated with the noise distribution and the change-point locations. In this paper, we handle this by deriving a lower bound on the mean square error of these continuous and discrete parameters. Specifically, we propose a hybrid Cramér–Rao–Weiss-Weinstein bound and derive its associated closed-form expressions. Numerical simulations assess the tightness of the proposed bound in the case of Gaussian and Poisson observations. [ABSTRACT FROM AUTHOR]

This paper studies a flexible approach to analyze high-dimensional nonlinear time series of unconstrained dimension based on linear statistics calculated from spectral average statistics of bilinear forms and nonlinear transformations of lag-window (i.e. band-regularized) spectral density matrix estimators. That class of statistics includes, among others, smoothed periodograms, nonlinear statistics such as coherency, long-run-variance estimators and contrast statistics related to factorial effects as special cases. Especially, we introduce the class of nonlinear spectral averages of the spectral density matrix. Having in mind big data settings, we study a sampling design which includes a sparse sampling scheme. Gaussian approximations with optimal rate are derived for nonlinear time series of growing dimension for these frequency domain statistics and the underlying lag-window (cross-) spectral estimator under non-stationarity. For change-testing (self-standardized) CUSUM statistics are examined. Further, a specific wild bootstrap procedure is proposed to estimate critical values. Simulation studies and an application to SP500 financial returns are provided in a supplement to this paper. [ABSTRACT FROM AUTHOR]

• Fire weather extremes in Europe have become more frequent in recent decades. • They affect increasingly larger areas and occur earlier/later in the fire season. • Increases in fire weather extremes are associated with significant change-points. • Atmospheric blocking and ridging drive fire weather extremes in Europe. • Increases in fire weather extremes challenge adaptation limits. Recent destructive fire seasons around the world have increased concern that climate change is escalating the frequency, intensity, and extent of wildfires. While the contributions of individual factors can be debated, the scientific literature concludes that fire weather is one prominent driver of fire activity. Our analysis of the relationship between fire weather extremes and burned area in Europe revealed that the number of extreme fire weather days per year correlates positively with the annual burned area over most of the study domain. The evidence presented in our study strongly suggests that fire weather extremes in Europe have become more frequent in recent decades while affecting increasingly larger areas and occurring earlier and later in the fire season. Increases in fire weather extremes occurred abruptly in several parts of Europe, as evidenced by the detection of statistically significant change-points in the time series of the annual extreme fire weather days. Our analysis revealed that, at the regional scale, significant change-points occurred around the late 1990s and mid-2000s in the Mediterranean, marking an abrupt rise in the median of extreme fire weather days per year. We found that high-latitude blocking and sub-tropical ridging drive fire weather extremes in northern and southern Europe, respectively. The results of our study suggest that fire weather extremes are today more likely to occur than in the past, and the increasing occurrence of extreme fire weather could lead to rapidly reaching adaptation limits, thereby reversing currently stagnating or even declining trends reported for fire activity in Europe. Corresponding with extreme fire weather years reported here, recent destructive fire seasons exemplify the limits of fire suppression and the need to adapt to the reality of more frequent extreme fire weather. [ABSTRACT FROM AUTHOR]

We discuss Fryzlewicz's paper that proposes WBS2.SDLL approach to detect possibly frequent changes in mean of a series. Our focus is on the potential issues related to the model misspecification. We present some numerical examples such as the self-exciting threshold autoregression and the unit root process, that can be confused as a frequent change-points model. [ABSTRACT FROM AUTHOR]

Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorizeminimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating process, we show that with high probability, the algorithm converges to a value that is within statistical error of the true change-point. A fast implementation of the algorithm using Markov Chain Monte Carlo is also introduced. The performances of the proposed algorithms are evaluated on synthetic data sets and the algorithm is also used to analyze structural changes in the S&P 500 over the period 2000-2016. [ABSTRACT FROM AUTHOR]

In this work, we introduce two novel methodologies to compute the envelope of superficial electromyography signals. Our methods are based on the detection of activation and deactivation patterns using a change-point approach on the variances of the sample. More concretely, an iterative algorithms is proposed to select the change-points between two segments of the signal based on some local statistics introduced in this work. The signal is split up into two segments, and a new search for change-points is recursively conducted in each subsequence. The change-points make possible to calculate local envelopes which reflect the shape of the signal without ignoring the activation and deactivation landmarks. Two methods are proposed in this work, and the improvements with respect to methodologies available in the literature are shown using both synthetic and real data. A thorough analysis of the techniques is performed to that end. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is acquiring increasing prominence as a cause of cancer death in the population. The purpose of this study was to analyze long-term pancreatic cancer mortality trends in Spain and evaluate the independent effects of age, death period and birth cohort on these trends.Methods: Population and mortality data for the period 1952-2012 were obtained from the Spanish National Statistics Institute. Pancreatic cancer deaths were identified using the International Classification of Diseases ICD-6 to ICD-9 (157 code) and ICD-10 (C25 code). Age-specific and age-adjusted mortality rates were computed by sex, region and five-year period. Changes in pancreatic cancer mortality trends were evaluated using joinpoint regression analyses by sex and region. Age-period-cohort log-linear models were fitted separately for each sex, and segmented regression models were used to detect changes in period- and cohort-effect curvatures.Results: In men, rates increased by 4.1% per annum from 1975 until the mid-1980s and by 1.1% thereafter. In women, there was an increase of 3.6% per annum until the late 1980s, and 1.4% per annum from 1987 to 2012. With reference to the cohort effects, there was an increase in mortality until the generations born in the 1950s in men and a subsequent decline detected by the change point in 1960. A similar trend was observed in women, but the change point occurred 10 years later than in men.Conclusions: Pancreatic cancer mortality increased over the study period in both sexes and all regions. An important rise in rates -around 4% annually- was registered until the 1980s, and upward trends were more moderate subsequently. The differences among sexes in trends in younger generations may be linked to different past prevalence of exposure to some risk factors, particularly tobacco, which underwent an earlier decrease in men than in women. [ABSTRACT FROM AUTHOR]

There is a global trend that the average onset age of many human complex diseases is decreasing, and the age of cancer patients becomes more spread out. The age effect on survival is nonlinear in practice and may have one or more important change-points at which the trend of the effect can be very different before and after these threshold ages. Identification of these change-points allows clinical researchers to understand the biologic basis for the complex relation between age and prognosis for optimal prognostic decision. This paper considers estimation of the potentially nonlinear age effect for general partly linear survival models to ensure a valid statistical inference on the treatment effect. A simple and efficient sieve maximum likelihood estimation method that can be implemented easily using standard statistical software is proposed. A data-driven adaptive algorithm to determine the optimal location and the number of knots for the identification of the change-points is suggested. Simulation studies are performed to study the performance of the proposed method. For illustration purpose, the method is applied to a breast cancer data set from the public domain to investigate the effect of onset age on the disease-free survival of the patients. The results revealed that the risk is highest among young patients and young postmenopausal patients, probably because of a change in hormonal environment during a certain phase of menopause. [ABSTRACT FROM AUTHOR]

Suppose observations y 1 , … , y n stem from a parametric model f ( y , θ ) , with the parameter taking one value θ L for y 1 , … , y τ and another value θ R for y τ + 1 , … , y n . This article provides and examines two different general strategies for not merely estimating the break point τ but also to complement such an estimate with full confidence distributions, both for the change-point τ and for associated measures of differences between the two levels of θ . The first idea worked with involves testing homogeneity for the two segments to the left and the right of a candidate change-point value at a fine-tuned level of significance. Carrying out such a scheme requires having a goodness-of-fit test for constancy of the θ parameter over a segment of indices, and we also develop classes of such tests. These also have some independent interest. The second general method uses the log-likelihood function, profiled over the other parameters, and we show how this may lead to confidence inference for τ . Our methods are illustrated for four real data stories, with these meeting different types of challenges. [ABSTRACT FROM AUTHOR]

Ecological thresholds represent a critical tipping point along an environmental gradient that, once breached, can have irreversible consequences for species persistence and assemblage structure. Thresholds can also be used to identify species with the greatest sensitivity to environmental changes. Bats are keystone species yet are under pressure from human disturbances, specifically landscape and cave disturbances (i.e., reduced forest cover, urbanization, hunting, tourism). We compared bat assemblages across environmental and disturbance gradients measured at 56 caves in the Philippines to identify species-specific thresholds and assess congruence among species responses. All species exhibited significant responses to one or more gradients, with 84% responding to more than one gradient. Yet mixed responses of sensitivity to some gradients but tolerance to others hindered identification of assemblage thresholds to all gradients except landscape disturbance. However, we identified credible indicator species that exhibit distinct thresholds to specific gradients and tested for differences in ecological and morphological traits between species groups with shared responses (i.e., negative or positive). Few traits were useful for discriminating the direction of a species response, with some exceptions. Species that responded positively to increased landscape disturbance and hunting had greater body mass, whereas species that responded negatively to mining emitted higher peak call frequencies.

The objective of this paper is to assess the possible influence of the 1994 In Trust Agreements (ITAs) on acquisition and distribution of germplasm held by the International Research Rice Institute (IRRI) genebank. The agreements, legally affirmed the ‘public good’ status of the collections that were placed ‘In Trust’ for the benefit of the world community under agreements with FAO. They initiated a formal system of multilateral access to CGIAR-held ex situ genetic resources. The hypothesis that the consequences of the ITAs lead to an enhancement of CGIAR germplasm utilization is tested here using a basic conceptual framework to infer on factors determining the distribution of germplasm. Subsequently a Bayesian empirical model is applied to IRRI accessions distribution’s time-series to provide formal evidence to the hypothesis. Results show that there is a discernible ‘change’ point that would support a significant drop in germplasm distribution followed by a new growing trend around the establishment of the ITAs. This had followed a period beginning around 1989 and leading up to the establishment of the ITAs of a large number of requests for restoration of germplasm back to countries of origin and a reduction in acquisitions. As a result the number of accessions held by IRRI reached a low point around 1994. The number of accessions might not have been built back up without the establishment of a stable policy environment that was provided by the ITAs.

We consider issues related to inference about locally ordered breaks in a system of equations, as originally proposed by Qu and Perron (2007). These apply when break dates in different equations within the system are not separated by a positive fraction of the sample size. This allows constructing joint confidence intervals of all such locally ordered break dates. We extend the results of Qu and Perron (2007) in several directions. First, we allow the covariates to be any mix of trends and stationary or integrated regressors. Second, we allow for breaks in the variance-covariance matrix of the errors. Third, we allow for multiple locally ordered breaks, each occurring in a different equation within a subset of equations in the system. Via some simulation experiments, we show first that the limit distributions derived provide good approximations to the finite sample distributions. Second, we show that forming confidence intervals in such a joint fashion allows more precision (tighter intervals) compared to the standard approach of forming confidence intervals using the method of Bai and Perron (1998) applied to a single equation. Simulations also indicate that using the locally ordered break confidence intervals yields better coverage rates than using the framework for globally distinct breaks when the break dates are separated by roughly 10% of the total sample size. [ABSTRACT FROM AUTHOR]

It is a common practice to analyze complex longitudinal data that arise frequently in medical studies that use nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models. However, the following issues may stand out in AIDS longitudinal data analysis: (i) In clinical practice, the profile of each subject’s virologic response may follow a “broken stick” like trajectory, indicating multiple phases of decline and increase in response. Such multiple phases (with change-points) may be an important indicator to help quantify treatment effect and improve management of patient care. To estimate change-points, NLME or NPME models become a challenge due to complicated structures of model formulations. (ii) A homogeneous population assumption in models may be unrealistically obscuring important features of between- and within-subject variations. (iii) Normality assumption for model errors may not always give robust and reliable results, in particular, if the data exhibit nonnormality. And (iv) the responses may be missing and the missingness may be nonignorable. When statistical inference is carried out in such settings, it is important to account for the effects of these data features; otherwise, erroneous or even misleading results may be produced. Inferential procedures can be complicated dramatically when data with heterogeneous, nonnormal (skewed) and missing characteristics are observed in conjunction with change-points as unknown parameters into models. There has been relatively little study concerning all these features simultaneously. There is a need to fill up this gap as longitudinal data do often have these features. In this article, our objectives are to study simultaneous impact of these data features by developing Bayesian modeling approach-based finite mixture of hierarchical change-point (FMHC) models with skew distributions, allowing estimates of both model parameters and class membership probabilities at population and individual levels. An HIV/AIDS clinical data example is analyzed to demonstrate the proposed methodologies, and to compare various scenarios-based potential models. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

We propose a new technique for consistent estimation of the number and locations of the change-points in the second-order structure of a time series. The core of the segmentation procedure is the Wild Binary Segmentation method (WBS), a technique which involves a certain randomised mechanism. The advantage of WBS over the standard Binary Segmentation lies in its localisation feature, thanks to which it works in cases where the spacings between change-points are short. In addition, we do not restrict the total number of change-points a time series can have. We also ameliorate the performance of our method by combining the CUSUM statistics obtained at different scales of the wavelet periodogram, our main change-point detection statistic, which allows a rigorous estimation of the local autocovariance of a piecewise-stationary process. We provide a simulation study to examine the performance of our method for different types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package wbsts, available from CRAN. [ABSTRACT FROM AUTHOR]