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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]

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]

Copyright of Revista do Serviço Público (Civil Service Review) is the property of Revista do Servico Publico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

Copyright of Colombian Journal of Statistics / Revista Colombiana de Estadística is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

This paper presents a new Bayesian methodology for identifying a transition period for the development of drug resistance to antiretroviral drug or therapy in HIV/AIDS studies or other related fields. Estimation of such a transition period requires an availability of longitudinal data where growth trajectories of a response variable tend to exhibit a gradual change from a declining trend to an increasing trend rather than an abrupt change. We assess this clinically important feature of the longitudinal HIV/AIDS data using the bent-cable framework within a growth mixture Tobit model. To account for heterogeneity of drug resistance among subjects, the parameters of the bent-cable growth mixture Tobit model are also allowed to differ by subgroups (subpopulations) of patients classified into latent classes on the basis of trajectories of observed viral load data with skewness and left-censoring. The proposed methods are illustrated using real data from an AIDS clinical study. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Rain precipitation in the last years has been very atypical in different regions of the world, possibly, due to climate changes. We analyze Standard Precipitation Index (SPI) measures (1, 3, 6 and 12-month timescales) for a large city in Brazil: Campinas located in the southeast region of Brazil, São Paulo State, ranging from January 01, 1947 to May 01, 2011. A Bayesian analysis of non-homogeneous Poisson processes in presence or not of change-points is developed using Markov Chain Monte Carlo methods in the data analysis. We consider a special class of models: the power law process. We also discuss some discrimination methods for the choice of the better model to be used for the rain precipitation data. [ABSTRACT FROM AUTHOR]

In this paper, we consider an estimation problem of the matrix of the regression coefficients in multivariate regression models with unknown change-points. More precisely, we consider the case where the target parameter satisfies an uncertain linear restriction. Under general conditions, we propose a class of estimators that includes as special cases shrinkage estimators (SEs) and both the unrestricted and restricted estimator. We also derive a more general condition for the SEs to dominate the unrestricted estimator. To this end, we extend some results underlying the multidimensional version of the mixingale central limit theorem as well as some important identities for deriving the risk function of SEs. Finally, we present some simulation studies that corroborate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article studies the estimation of a stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out an extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. A detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array comparative genomic hybridization (CGH) data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Studies of human immunodeficiency virus dynamics in acquired immuno deficiency syndrome (AIDS) research are very important in evaluating the effectiveness of antiretroviral (ARV) therapies. The potency of ARV agents in AIDS clinical trials can be assessed on the basis of a viral response such as viral decay rate or viral load change in plasma. Following ARV treatment, the profile of each subject's viral load tends to follow a 'broken stick'-like dynamic trajectory, indicating multiple phases of decline and increase in viral loads. Such multiple-phases (change-points) can be described by a random change-point model with random subject-specific parameters. One usually assumes a normal distribution for model error. However, this assumption may be unrealistic, obscuring important features of within- and among-subject variations. In this article, we propose piecewise linear mixed-effects models with skew-elliptical distributions to describe the time trend of a response variable under a Bayesian framework. This methodology can be widely applied to real problems for longitudinal studies. A real data analysis, using viral load data from an AIDS study, is carried out to illustrate the proposed method by comparing various candidate models. Biologically important findings are reported, and these findings also suggest that it is very important to assume a model with skew distribution in order to achieve reliable results, in particular, when the data exhibit skewness. [ABSTRACT FROM AUTHOR]

The proposed models consider multiple change-points in software testing/debugging process with time-varying learning effects and are able to reasonably describe the S and exponential-shaped debugging process, simultaneously. The proposed models included both linear and exponential learning functions in the software reliability growth models to predict the detected errors and removed errors, judge change-points by the lag time between the errors-detected and errors-removed, and discuss the parameters of learning effects with change-points in the testing process by actual data-sets. The results show those change-points usually occur when the lag time between the errors-detected and errors-removed has material change. This study also verifies the effectiveness of the proposed models with R2 and mean square error (MSE), and compares the results with those of other models using actual data-sets. The proposed models have a better fit and are more reasonable to describe the actual data. [ABSTRACT FROM AUTHOR]

We consider the problem of learning the inhomogeneous intensity of a counting process, under a sparse segmentation assumption. We introduce a weighted total-variation penalization, using data-driven weights that correctly scale the penalization along the observation interval. We prove that this leads to a sharp tuning of the convex relaxation of the segmentation prior, by stating oracle inequalities with fast rates of convergence, and consistency for change-points detection. This provides first theoretical guarantees for segmentation with a convex proxy beyond the standard independent identically distributed signal + white noise setting. We introduce a fast algorithm to solve this convex problem. Numerical experiments illustrate our approach on simulated and on a high-frequency genomics data set. [ABSTRACT FROM AUTHOR]

We assess the existence of duration dependence in the likelihood of an end in housing booms, busts, and normal times. Using data for 20 industrial countries and a continuous-time Weibull duration model, we find evidence of positive duration dependence suggesting that housing market cycles have become longer over the last decades. Then, we extend the baseline Weibull model and allow for the presence of a change-point in the duration dependence parameter. We show that positive duration dependence is present in booms and busts that last less than 26 quarters, but that does not seem to be the case for longer phases of the housing market cycle. For normal times, no evidence of change-points is found. Finally, the empirical findings uncover positive duration dependence in housing market booms of European and non-European countries and housing busts of European countries. In addition, they reveal that while housing booms have similar length in European and non-European countries, housing busts are typically shorter in European countries. [ABSTRACT FROM PUBLISHER]

In this paper, we study an inference problem for the regression coefficients in some multivariate regression models with multiple change-points occurring at unknown times, when the regression coefficients may satisfy some restrictions. The hypothesized restriction is more general than that given in recent literature. Under a weaker assumption than that given in recent literature, we derive the joint asymptotic normality of the restricted and unrestricted estimators. Finally, we construct a test for the hypothesized restriction and derive its asymptotic power. [ABSTRACT FROM AUTHOR]

We propose nonparametric procedures for testing change-point by using the ℙ-ℙ and ℚ-ℚ plots processes. The limiting distributions of the proposed statistics are characterized under the null hypothesis of no change and also under contiguous alternatives. We give an estimator of the change-point coefficient and obtain its strong consistency. We introduce the bootstrapped version of ℙ-ℙ and ℚ-ℚ processes, requiring the estimation of quantile density, and obtain their limiting laws. Finally, we propose and investigate the exchangeable bootstrap of the empirical ℙ-ℙ plot and ℚ-ℚ plot processes which avoids the problem of the estimation of quantile density, which is of its own interest. These results are used for calculatingp-values of the proposed test statistics. Emphasis is placed on the explanation of the strong approximation methodology. [ABSTRACT FROM AUTHOR]

Consider a structural break autoregressive (SBAR) processwherej= 1, …,m+ 1, {t1, …,tm} are change-points, 1 =t0

The paper considers a linear regression model with multiple change-points occurring at unknown times. The LASSO technique is very interesting since it allows simultaneously the parametric estimation, including the change-points estimation, and the automatic variable selection. The asymptotic properties of the LASSO-type (which has as particular case the LASSO estimator) and of the adaptive LASSO estimators are studied. For this last estimator the Oracle properties are proved. In both cases, a model selection criterion is proposed. Numerical examples are provided showing the performances of the adaptive LASSO estimator compared to the least squares estimator. [ABSTRACT FROM AUTHOR]

Background: Although oral cavity, pharyngeal, oesophageal and gastric cancers share some risk factors, no comparative analysis of mortality rate trends in these illnesses has been undertaken in Spain. This study aimed to evaluate the independent effects of age, death period and birth cohort on the mortality rates of these tumours. Methods: Specific and age-adjusted mortality rates by tumour and sex were analysed. Age-period-cohort log-linear models were fitted separately for each tumour and sex, and segmented regression models were used to detect changes in period- and cohort-effect curvatures. Results: Among men, the period-effect curvatures for oral cavity/pharyngeal and oesophageal cancers displayed a mortality trend that rose until 1995 and then declined. Among women, oral cavity/pharyngeal cancer mortality increased throughout the study period whereas oesophageal cancer mortality decreased after 1970. Stomach cancer mortality decreased in both sexes from 1965 onwards. Lastly, the cohort-effect curvature showed a certain degree of similarity for all three tumours in both sexes, which was greater among oral cavity, pharyngeal and oesophageal cancers, with a change point in evidence, after which risk of death increased in cohorts born from the 1910-1920s onwards and decreased among the 1950-1960 cohorts and successive generations. This latter feature was likewise observed for stomach cancer. Conclusions: While the similarities of the cohort effects in oral cavity/pharyngeal, oesophageal and gastric tumours support the implication of shared risk factors, the more marked changes in cohort-effect curvature for oral cavity/pharyngeal and oesophageal cancer could be due to the greater influence of some risk factors in their aetiology, such as smoking and alcohol consumption. The increase in oral cavity/pharyngeal cancer mortality in women deserves further study. [ABSTRACT FROM AUTHOR]

Background: In contrast to other haematological cancers, mortality from non-Hodgkin's lymphoma and multiple myeloma increased dramatically during the second half of the 20th century in most developed countries. This widespread upward trend remains controversial, as it may be attributable either to progressive improvements in diagnosis and certification or to increasing exposures to little-known but relevant risk factors. Methods: To assess the relative contribution of these factors, we analysed the independent effects of age, death period, and birth cohort on haematological cancer mortality rates in Spain across the period 1952-2006. Weighted joinpoint regression analyses were performed to detect and estimate changes in period and cohort curvatures. Results: Although mortality rates were consistently higher among men, trends across periods and cohorts were virtually identical in both sexes. There was an early period trend reversal in the 1960s for Hodgkin's disease and leukaemia, which was delayed to the 1980s for multiple myeloma and the 1990s for non-Hodgkin's lymphoma. Birth cohort patterns showed a first downturn for generations born in the 1900s and 1910s for all haematological cancers, and a second trend reversal for more recent cohorts born in the 1950s and 1960s for non-Hodgkin's lymphoma and leukaemia. Conclusions: The sustained decline in Hodgkin's disease mortality and the levelling off in leukaemia seem to be driven by an early period effect linked to improvements in disease treatment, whereas the steep upward trends in non-Hodgkin's lymphoma and multiple myeloma mortality in Spain are more likely explained by a cohort effect linked to better diagnosis and death certification in the elderly. The consistent male excess mortality across all calendar periods and age groups points to the importance of possible sex-related genetic markers of susceptibility in haematological cancers. [ABSTRACT FROM AUTHOR]

Background: In contrast to other haematological cancers, mortality from non-Hodgkin's lymphoma and multiple myeloma increased dramatically during the second half of the 20th century in most developed countries. This widespread upward trend remains controversial, as it may be attributable either to progressive improvements in diagnosis and certification or to increasing exposures to little-known but relevant risk factors. Methods: To assess the relative contribution of these factors, we analysed the independent effects of age, death period, and birth cohort on haematological cancer mortality rates in Spain across the period 1952-2006. Weighted joinpoint regression analyses were performed to detect and estimate changes in period and cohort curvatures. Results: Although mortality rates were consistently higher among men, trends across periods and cohorts were virtually identical in both sexes. There was an early period trend reversal in the 1960s for Hodgkin's disease and leukaemia, which was delayed to the 1980s for multiple myeloma and the 1990s for non-Hodgkin's lymphoma. Birth cohort patterns showed a first downturn for generations born in the 1900s and 1910s for all haematological cancers, and a second trend reversal for more recent cohorts born in the 1950s and 1960s for non-Hodgkin's lymphoma and leukaemia. Conclusions: The sustained decline in Hodgkin's disease mortality and the levelling off in leukaemia seem to be driven by an early period effect linked to improvements in disease treatment, whereas the steep upward trends in non-Hodgkin's lymphoma and multiple myeloma mortality in Spain are more likely explained by a cohort effect linked to better diagnosis and death certification in the elderly. The consistent male excess mortality across all calendar periods and age groups points to the importance of possible sex-related genetic markers of susceptibility in haematological cancers. [ABSTRACT FROM AUTHOR]

Background: Although oral cavity, pharyngeal, oesophageal and gastric cancers share some risk factors, no comparative analysis of mortality rate trends in these illnesses has been undertaken in Spain. This study aimed to evaluate the independent effects of age, death period and birth cohort on the mortality rates of these tumours. Methods: Specific and age-adjusted mortality rates by tumour and sex were analysed. Age-periodcohort log-linear models were fitted separately for each tumour and sex, and segmented regression models were used to detect changes in period- and cohort-effect curvatures. Results: Among men, the period-effect curvatures for oral cavity/pharyngeal and oesophageal cancers displayed a mortality trend that rose until 1995 and then declined. Among women, oral cavity/pharyngeal cancer mortality increased throughout the study period whereas oesophageal cancer mortality decreased after 1970. Stomach cancer mortality decreased in both sexes from 1965 onwards. Lastly, the cohort-effect curvature showed a certain degree of similarity for all three tumours in both sexes, which was greater among oral cavity, pharyngeal and oesophageal cancers, with a change point in evidence, after which risk of death increased in cohorts born from the 1910-1920s onwards and decreased among the 1950-1960 cohorts and successive generations. This latter feature was likewise observed for stomach cancer. Conclusions: While the similarities of the cohort effects in oral cavity/pharyngeal, oesophageal and gastric tumours support the implication of shared risk factors, the more marked changes in cohorteffect curvature for oral cavity/pharyngeal and oesophageal cancer could be due to the greater influence of some risk factors in their aetiology, such as smoking and alcohol consumption. The increase in oral cavity/pharyngeal cancer mortality in women deserves further study. [ABSTRACT FROM AUTHOR]

In a smoothing spline model with unknown change-points, the choice of the smoothing parameter strongly influences the estimation of the change-point locations and the function at the change-points. In a tumor biology example, where change-points in blood flow in response to treatment were of interest, choosing the smoothing parameter based on minimizing generalized cross-validation (GCV) gave unsatisfactory estimates of the change-points. We propose a new method, aGCV, that re-weights the residual sum of squares and generalized degrees of freedom terms from GCV. The weight is chosen to maximize the decrease in the generalized degrees of freedom as a function of the weight value, while simultaneously minimizing aGCV as a function of the smoothing parameter and the change-points. Compared with GCV, simulation studies suggest that the aGCV method yields improved estimates of the change-point and the value of the function at the change-point. [ABSTRACT FROM PUBLISHER]

In this paper, we consider several modelling approaches for the mean time between exceedances of a given environmental threshold. The interest here resides in the time between ozone exceedances (also called ozone inter-exceedances times). The proposed models assume two basic density functions for the time between surpassings: the Weibull and the generalised exponential functions. Considering those distributions, a random effect with autoregressive structure is taken into account to determine unexpected changes in the mean of the inter-exceedances density functions. Those unexpected changes could be captured either by their scale parameter or by both their scale and shape parameters. The models are applied to ozone data from the monitoring network of Mexico City. Selection of the model that best explains the data is performed using the deviance information criterion and also the sum of the absolute values of the differences between the estimated and observed means of the inter-exceedances times. An analysis to detect the possible presence of change-points is also presented. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the timing of structural changes in yield curve dynamics in the context of an arbitrage-free, one latent and two macroeconomic factors, affine term structure model. We suppose that all parameters in the model are subject to changes at unknown time points. We fit a number of models to the U.S. term structure data and find support for three change-points. We also find that the term structure and the risk premium are materially different across regimes and that the out-of-sample forecasts of the term structure improve from incorporating regime changes. [ABSTRACT FROM PUBLISHER]

This paper gives an account of some of the recent work on structural breaks in time series models. In particular, we show how procedures based on the popular cumulative sum, CUSUM, statistics can be modified to work also for data exhibiting serial dependence. Both structural breaks in the unconditional and conditional mean as well as in the variance and covariance/correlation structure are covered. CUSUM procedures are nonparametric by design. If the data allows for parametric modeling, we demonstrate how likelihood approaches may be utilized to recover structural breaks. The estimation of multiple structural breaks is discussed. Furthermore, we cover how one can disentangle structural breaks (in the mean and/or the variance) on one hand and long memory or unit roots on the other. Several new lines of research are briefly mentioned. [ABSTRACT FROM AUTHOR]

This paper considers two-phase random design linear regression models. Errors and regressors are stationary long-range-dependent Gaussian processes. The regression parameters, the scale parameter and the change-point are estimated using a method introduced by Rousseeuw and Yohai [Robust regression by means of S-estimators, in Robust and Nonlinear Time Series Analysis, J. Franke, W. Hrdle, and R.D. Martin, eds., Lecture Notes in Statistics, Vol. 26, Springer, New York, 1984, pp. 256–272], which is called the S-estimator and has the property be more robust than the classical estimators in the sense that the outliers do not bias the estimation results. Some asymptotic results, including the strong consistency and the convergence rate of the S-estimator are proved. Simulations and an application to the Nile River data are also presented. It is shown via Monte Carlo simulations that the S-estimator is better than two other estimators that are proposed in the literature. [ABSTRACT FROM AUTHOR]

A new approach based on the fit of a generalized linear regression model is introduced for detecting change-points in the variance of heteroscedastic Gaussian variables, with piecewise constant variance function. This approach overcome some limitations of both exact and approximate well-known methods that are based on successive application of search and tend to overestimate the real number of changes in the variance of the series. The proposed method just requires the computation of a gamma GLM with log-link, resulting in a very efficient algorithm even with large sample size and many change points to be estimated. [ABSTRACT FROM PUBLISHER]

The relation between change points in multivariate surveillance is important but seldom considered. The sufficiency principle is here used to clarify the structure of some problems, to find efficient methods, and to determine appropriate evaluation metrics. We study processes where the changes occur simultaneously or with known time lags. The surveillance of spatial data is one example where known time lags can be of interest. A general version of a theorem for the sufficient reduction of processes that change with known time lags is given. A simulation study illustrates the benefits or the methods based on the sufficient statistics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]