Change-point Model Research Articles

MountainClimber Identifies Alternative Transcription Start and Polyadenylation Sites in RNA-Seq.

Alternative transcription start (ATS) and alternative polyadenylation (APA) create alternative RNA isoforms and modulate many aspects of RNA expression and protein production. However, ATS and APA remain difficult to detect in RNA sequencing (RNA-seq). Here, we developed mountainClimber, a de novo cumulative-sum-based approach to identify ATS and APA as change points. Unlike many existing methods, mountainClimber runs on a single sample and identifies multiple ATS or APA sites anywhere in the transcript. We analyzed 2,342 GTEx samples (36 tissues, 215 individuals) and found that tissue type is the predominant driver of transcript end variations. 75% and 65% of genes exhibited differential APA and ATS across tissues, respectively. In particular, testis displayed longer 5' untranslated regions (UTRs) and shorter 3' UTRs, often in genes related to testis-specific biology. Overall, we report the largest study of transcript ends across human tissues to our knowledge. mountainClimber is available at github.com/gxiaolab/mountainClimber.

A statistical learning exercise based on a modified Rock-Paper-Scissors game

The standard version of the game Rock-Paper-Scissors is interesting in terms of game theory, but less so in terms of Statistics. However, we show that with a small rule change it can be made into an interactive exercise for degree-level students of Statistics that leads to a Bayesian change-point model, for which the Gibbs sampler provides an intuitive method of inference. First, students play the game to generate the data. Second, they are encouraged to formulate a model that reflects their experience from having played the game. And third, they participate in the development of a suitable MCMC algorithm to fit the model.

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection.

When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014-2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

Semiparametric Multivariate and Multiple Change-Point Modeling

We develop a general Bayesian semiparametric change-point model in which separate groups of structural parameters (for example, location and dispersion parameters) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes is unknown and given by a Dirichlet process mixture prior. The properties of the proposed model are studied theoretically through the analysis of inter-arrival times and of the number of change-points in a given time interval. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a forward-backward algorithm for sampling the various regime indicators. Analysis with simulated data under various scenarios and an application to short-term interest rates are used to show the generality and usefulness of the proposed model.

Spatio-temporal analysis of differences in campylobacteriosis incidence between urban and rural areas in the Southern District Health Board, New Zealand

The objective of this paper is to investigate differences in campylobacteriosis incidence between urban and rural areas in the Southern District Health Board of New Zealand between 2000 and 2015. The data were analysed using a Bayesian change-point model to evaluate how campylobacteriosis incidence changed over time and to see whether the dynamics differed between rural and urban areas. A conditional auto regressive error term was introduced to account for any spatial effects. The results of our analysis showed that campylobacteriosis incidence increased between 2000 and 2005, decreased between 2006 and 2008 then stabilised from 2009 onward. In addition we found that the changes in incidence were greater in urban areas than in rural ones.

Reliability modeling for a two-phase degradation system with a change point based on a Wiener process

A two-phase degradation system with a change point is commonly seen in many real-world degradation systems due to influence of internal mechanisms and external environments, such as lithium-ion batteries, light-emitting diodes (LEDs), plasma display panels (PDPs), and vacuum fluorescent displays (VFDs). Random jumps exist at the change points of the degradation processes. Motivated by this phenomenon, this paper attempts to model such kinds of systems based on the Wiener process. Specifically, In Model 1, a constant change point model is first considered. The analytical results of the system reliability, lifetime distribution, and the remaining useful lifetime (RUL) are obtained. Then, based on Model 1, a random change point model, i.e., Model 2, is proposed to explain the change point phenomenon which is influenced by the accumulative effect of a shock process. Two phase-change patterns (PCPs) are considered in Model 2. For PCP I, the change point is triggered when the number of shocks reaches a predetermined threshold. For PCP II, the change point is triggered when the accumulative shock damage exceeds the corresponding threshold. The system reliability indexes are studied by using analytical and simulation methods. Finally, several numerical examples are given to illustrate the approaches and results.

A change-point model for monitoring the coefficient of variation based on squared ranks test

Monitoring the coefficient of variation tends to be established as an efficient approach of the statistical process control in case of simultaneously fluctuating mean and variance. However, the recent research is constricted to develop control charts for monitoring the coefficient of variance based on normality assumption. This paper presents a new efficient method to monitor the coefficient of variation implementing the well known distribution-free square ranks test through a change-point model. Extensive numerical analysis is presented to evaluate the performance of the proposed change-point model alongside with tables showing its statistical run length properties and comparison examples with conventional control charts.

Identifying phenological phases in strawberry using multiple change-point models

Plant development studies often generate data in the form of multivariate time series, each variable corresponding to a count of newly emerged organs for a given development process. These phenological data often exhibit highly structured patterns, and the aim of this study was to identify such patterns in cultivated strawberry. Six strawberry genotypes were observed weekly for their course of emergence of flowers, leaves, and stolons during 7 months. We assumed that these phenological series take the form of successive phases, synchronous between individuals. We applied univariate multiple change-point models for the identification of flowering, vegetative development, and runnering phases, and multivariate multiple change-point models for the identification of consensus phases for these three development processes. We showed that the flowering and the runnering processes are the main determinants of the phenological pattern. On this basis, we propose a typology of the six genotypes in the form of a hierarchical classification. This study introduces a new longitudinal data modeling approach for the identification of phenological phases in plant development. The focus was on development variables but the approach can be directly extended to growth variables and to multivariate series combining growth and development variables.

Decreased Mortality After Establishing a Pediatric Emergency Unit at an Urban Referral Hospital in Ghana.

Emergently ill infants and children are often inadequately recognized and stabilized by health care facilities in low- and middle-income countries. Limited reports have shown that process improvements and prioritization of emergency care for children presenting to the hospital can improve pediatric hospital mortality.A dedicated pediatric emergency unit (PEU) was established for nontrauma emergencies at a busy teaching and referral hospital in Kumasi, Ghana, in response to high inpatient mortality early during hospitalization. The PEU was designed to identify and separate critically ill children from more stable children on admission. Locally available hospital resources were reallocated from other areas of the hospital to prioritize staffing and supplies for the PEU.A multiyear data set of nonnewborn inpatient mortality was analyzed with a change point model to find the point at which mortality changed the most within the Department of Child Health or the maximum likelihood estimate. Relative risk of mortality for the periods 1 and 2 years immediately before and after the implementation of the PEU and each individual year compared with its preceding year was analyzed to further establish a temporal correlation of changes in mortality rates to the PEU implementation. Individual years were also analyzed against preimplementation data to establish the durability of mortality improvements.Patient mortality decreased over the analyzed period with the maximum change point strongly associated with implementation of the PEU. Relative risk values of mortality 1 year and 2 years immediately before and after implementation of the PEU were 0.70 (0.62-0.78) and 0.69 (0.64-0.74) respectively, representing a one-third reduction in mortality. The only other mortality improvements seen in the year-to-year analysis were between July 2004-June 2005 compared with July 2005-June 2006 with a relative risk of 0.86 (0.77-0.96).Prioritizing and redirecting limited resources toward pediatric emergency care in low- and middle-income country hospitals is associated with reductions in inpatient mortality that are both immediate and sustained.

Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football

ABSTRACTWe examine the effects of the 3-points-for-a-win (3pfaw) rule in the football world. Data that form the basis of our analyses come from seven leagues around the world (Albania, Brazil, England, Germany, Poland, Romania, and Scotland) and consist of mean goals and proportions of decided matches over a period of about six years before- and about seven years after the introduction of the rule in the respective leagues. Bayesian change-point analyses and Shiryaev-Roberts tests show that the rule had no effects on the mean goals but, indeed, had increasing effects on the proportions of decided matches in most of the leagues studied. This, in turn, implies that while the rule has given teams the incentive to aim at winning matches, such aim was not achieved by scoring excess goals. Instead, it was achieved by scoring enough goals in order to win and, at the same time, defending enough in order not to lose. Our results are in accordance with recent findings on comparing the values of attack and defense - that, in top-level football, not conceding a goal is more valuable than scoring a single goal.

Estimation in the single change-point hazard function for interval-censored data with a cure fraction

ABSTRACTIn reliability or survival analysis, the hazard function plays a significant part for it can display the instantaneous failure rate at any time point. In practice, the abrupt change in hazard function at an unknown time point may occur after a maintenance activity or major operation. Under these circumstances, identifying the change point and estimating the size of the change are meaningful. In this paper, we assume that the hazard function is piecewise constant with a single jump at an unknown time. We propose the single change-point model for interval-censored survival data with a cure fraction. Estimation methods for the proposed model are investigated, and large-sample properties of the estimators are established. Simulation studies are carried out to evaluate the performance of the estimating method. The liver cancer data and breast cancer data are analyzed as the applications.

Sequential Monitoring for Speculative Bubbles in Financial Time Series

We develop and study sequential testing procedures a la Chu et al. (1996) for on-line detection of changes in a time series from stationarity to mild forms of non-stationarity. The proposed tests are based on sequential CUSUM and KPSS-type detector processes, and are shown to provide consistent detection under a wide range of change point models, including changes in the parameters of ARMA and GARCH series from values within the model's stationarity parameter region to values close (converging) to the stationarity boundary. Local asymptotic results are established giving precise descriptions of the time to detection under several of these models, which show that such procedures are powerful to detect a wide range of non-stationary characteristics, including changes in mean, volatility, and unit root behaviour. The proposed methods are investigated by means of a simulation study and in applications to monitoring for changes in trend and unit root behaviour in macroeconomic production series, and to detect changes in volatility of the S&P-500 stock market index.

A Change-point Regression Approach for Efficacy Evaluation of Dietary Supplements (P13-017-19)

ObjectivesIn clinical trials for dietary supplements and functional foods, the study population tends to be a mixture of healthy subjects and those who are not so healthy but are not definitely diseased (called “borderline subjects”). For such heterogeneous populations, the t-test and ANCOVA method often fail to provide the desired treatment efficacy. We propose an alternative approach for the efficacy evaluation of dietary supplements and functional foods based on a change-point linear regression model. MethodsSuppose we are interested in analyzing data from a randomized controlled clinical trial of dietary supplements which was conducted under what is called pre-post design. We propose the change-point regression model (CPRM) as new analysis method. The model does not require the assumption of a constant treatment effect and provides clinically interpretable results. By employing the AIC-based profile likelihood method, inferences can be made easily using standard statistical software. ResultsCPRM method was applied to (-)-hydroxycitric acid (HCA) study data, and the merit of the method was demonstrated by comparing it with traditional methods those are ANCOVA or t-test. The AICs of CPRM, ANCOVA and t-test were 667.2, 685.9 and 685.0 respectively. Therefore, CPRM is well fit than traditional methods. Advantage of CPRM is that it can simultaneously provide information on effect appearance points and we succeeded estimation of the point in (-)-HCA study. ConclusionsWe propose an alternative approach for the efficacy evaluation of dietary supplements and functional foods based on a CPRM. By employing the AIC-based profile likelihood method, inferences can be made easily using standard statistical software. The proposed method was applied to the Garcinia study data, and the merit of the method was demonstrated by comparing it with the ANCOVA models. Funding SourcesThis research did not received any specific grant from funding agencies in the public. commercial, or not for-profit sectors.

A penalized regression approach for DNA copy number study using the sequencing data.

Modeling the high-throughput next generation sequencing (NGS) data, resulting from experiments with the goal of profiling tumor and control samples for the study of DNA copy number variants (CNVs), remains to be a challenge in various ways. In this application work, we provide an efficient method for detecting multiple CNVs using NGS reads ratio data. This method is based on a multiple statistical change-points model with the penalized regression approach, 1d fused LASSO, that is designed for ordered data in a one-dimensional structure. In addition, since the path algorithm traces the solution as a function of a tuning parameter, the number and locations of potential CNV region boundaries can be estimated simultaneously in an efficient way. For tuning parameter selection, we then propose a new modified Bayesian information criterion, called JMIC, and compare the proposed JMIC with three different Bayes information criteria used in the literature. Simulation results have shown the better performance of JMIC for tuning parameter selection, in comparison with the other three criterion. We applied our approach to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL 1954 and the results are in-line with those discovered in the literature.

Bayesian Log-Bilinear Mortality Projection with a Random Walk with Drift

This paper extends the widely used Lee Carter (LC) model (Lee & Carter, 1992) for mortality projection. We suggest a random walk with drift to model the time parameter of the Bayesian extension of the LC model suggested in Czado et al. (2005). In a validation-based examination, the proposed random walk model produces smaller prediction errors compared to the autoregressive model in Czado et al. (2005). The random walk with drift model is valid only if the linear trend remains constant over time. We further extend the random-walk model to allow for change-points in the time parameter. In a validation-based examination, the proposed change-points model produces smaller prediction errors compared to a model with constant drift parameter.

Timing of onset and rate of decline in learning and retention in the pre-dementia phase of Alzheimer's disease.

To examine trajectories of declines in learning and retention during the predementia phase of Alzheimer's disease (AD) using the picture version of the Free and Cued Selective Reminding Test with Immediate Recall (pFCSRT+IR). Learning was defined by the sum of free recall over three test trials. Retention was defined in two ways: by delayed free recall (DFR) and by savings; DFR adjusted for learning. The performances of 217 incident AD cases from the Baltimore Longitudinal Study of Aging (BLSA) were aligned based on the time that AD was first diagnosed. The predementia phase of learning and retention decline was assessed using change point models in which cognitive trajectories are described by a series of linear components with knots delineating times of accelerating decline. Trajectories for both learning and DFR had two change points: the first at 6.58 (95% confidence intervals (CI): 6.56, 6.60) to 7.29 (95% CI: 6.13, 8.46) years before diagnosis followed by gradual decline over the next 4 years, and a second acceleration of decline 1.89 (0.56, 3.24) to 2.93 (95% CI: 1.56, 4.30) years before diagnosis. The change points for DFR were not significantly earlier in the predementia phase than the change points for learning. Savings had one change point, 5.3 (95% CI: 3.56, 7.04) years before diagnosis. Both learning and DFR showed similar profiles of decline in the years prior to the clinical diagnosis of AD. When delayed recall was adjusted for initial learning, the measure was less sensitive to early disease. (JINS, 2019, 25, 699-705).

Longitudinal Macular Structure-Function Relationships in Glaucoma and Their Sources of Variability

To review central structure-function (SF) relationships in glaucoma; to compare contributions of within-session and between-session variability to total variability of macular optical coherence tomography (OCT) thickness measurements; and to test the hypothesis that longitudinal within-eye variability of central SF relationships is smaller than between-individual variability. We reviewed the pertinent literature on central SF relationships in glaucoma. Thirty-eight eyes (20 normal or glaucoma subjects) had×3 macular images per session over 3 sessions, and superpixels thickness measurements for ganglion cell layer (GCL), ganglion cell/inner plexiform layer (GCIPL), ganglion cell complex (GCC), and full macular thickness (FMT) were exported. Linear mixed models were used for estimating contributions of between- and within-session variability to total thickness variability. One hundred twenty eyes with ≥3 10° visual fields (VFs)/OCT images were enrolled for the longitudinal study. We investigated within-eye longitudinal SF relationships (GCIPL thickness vs VF total deviations) with a change-point regression model and compared within-eye to between-individual variabilities with components-of-variance models. In the cross-sectional study, the between-session component contributed 8%, 11%, 11%, and 36% of total variability for GCL, GCIPL, GCC, and FMT, respectively. In the longitudinal study, between-individual variability explained 78%, 77%, and 67% of total SF variability at 3.4°, 5.6°, and 6.8° eccentricities, respectively (P < .05). SF relationships remained stable over time within individual eyes. Within-session variability accounts formost of macular thickness variability over time. Longitudinal within-eye SF variability is smaller than between-individual variability. Study of within-eye SF relationships could help clinicians better understand SF linking in glaucoma and help refine progression algorithms. NOTE: Publication of this article is sponsored bythe American Ophthalmological Society.

Improvement of inverse change-point modeling of electricity consumption in residential buildings across multiple climate zones

Inverse modeling is a common method to predict electricity consumption in buildings. Residential building electricity consumption patterns can vary significantly due to occupants and their sporadic energy-consuming behaviors, as well as due to variations in HVAC system types and characteristics across climate zones. However most data-driven methods in this area have been developed and evaluated using limited datasets. This points to the need for an understanding of how well data-driven models perform using residential energy consumption data from a range of locations and home types using a diverse dataset. Thus in this research, first, inverse change-point modeling methods are used to develop predictive models of monthly electricity use for 3,643 houses in four U.S. cities in three ASHRAE climate zones (2A, 4A, 5A), to evaluate the model performance. However, approximately 40% of homes did not fit within recommended criteria for change-point model development following a common model development sequence. Therefore, a modified version of the sequence, including a segmented change-point model, is then developed and evaluated. Change-point models with relaxed prerequisite criteria are also used to enable the fitting of models to a larger number of homes’ data. As a result of these modifications, the number of homes with models increased from 60% to 71%, with a goodness-of-fit improvement of 13% (RMSE) and 8% (CV-RMSE) across the datasets evaluated. The results of this work enable improved prediction of energy use across a diversity of buildings and climate zones.

Detection and interpretation of anomalies in building energy use through inverse modeling

This article presents a study in which three inverse modeling techniques were applied to hourly heating and cooling load data extracted from 35 office buildings in Ottawa, Canada. These modeling techniques were three-parameter change point models, regression trees, and artificial neural networks. The change point models were trained with outdoor temperature data, whereas the other two models were trained with four regressors: outdoor temperature, wind speed, horizontal solar irradiance, and a binary work hours indicator. The correlations among the change point model parameters of individual buildings were analyzed. The sensitivity of heating and cooling load intensities to the four regressors was examined. The models were used to identify several types of energy use anomalies. The anomalies detected by different modeling techniques were generally in agreement. The results indicate that nearly half of the buildings did not have effective after-hours schedules to save energy. In all but three buildings, the cooling change point temperature was lower than the heating change point temperature—indicating a simultaneous heating and cooling problem. Moreover, a few buildings with anomalies potentially related to high air infiltration or overventilation, high thermal conductance, and high solar heat gains during summer were identified.

Change-point analysis of the failure mechanisms based on accelerated life tests

In the accelerated life tests, the common failure mechanism is considered as a necessary condition for the extrapolated procedure. The traditional extrapolation model may become unreliable if the failure mechanisms under the accelerated stress levels are different from that under the normal operating condition. In this paper, we propose a change-point model for the coefficients of variation to fit the abrupt change behavior of the failure mechanisms with a nonparametric empirical likelihood approach. The related statistical inferences of the proposed model are studied to test whether there exists a change and estimate the corresponding location of the change. Monte Carlo simulations are conducted to investigate the performance of the proposed change-point test model. For the small sample data, a bootstrapping method is presented as an alternative detecting procedure. The detailed calculation process is illustrated by the lifetime data of the metal oxide semiconductor transistors in the power distribution system of Chinese Tiangong aircrafts.