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Change Point (change + point)

Distribution by Scientific Domains

Mathematics and Statistics	35%
Engineering	22%
Earth and Environmental Science	19%
Life Sciences	6%
Business, Economics, Finance and Accounting	6%

Selected Abstracts

ON THE CHANGE POINT OF THE MEAN RESIDUAL LIFE OF SERIES AND PARALLEL SYSTEMS

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2010
Yan Shen
Summary This paper considers the mean residual life in series and parallel systems with independent and identically distributed components and obtains relationships between the change points of the mean residual life of systems and that of their components. Compared with the change point for single components, should it exists, the change point for a series system occurs later. For a parallel system, however, the change point is located before that for the components, if it exists at all. Moreover, for both types of systems, the distance between the change points of the mean residual life for systems and for components increases with the number of components. These results are helpful in the determination of optimal burn-in time and related decision making in reliability analysis. [source]

Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum

ECONOMETRICA, Issue 1 2003
Donald W. K. Andrews
No abstract is available for this article. [source]

Estimating the Change Point of a Poisson Rate Parameter with a Linear Trend Disturbance

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2006
Marcus B. Perry
Abstract Knowing when a process changed would simplify the search and identification of the special cause. In this paper, we compare the maximum likelihood estimator (MLE) of the process change point designed for linear trends to the MLE of the process change point designed for step changes when a linear trend disturbance is present. We conclude that the MLE of the process change point designed for linear trends outperforms the MLE designed for step changes when a linear trend disturbance is present. We also present an approach based on the likelihood function for estimating a confidence set for the process change point. We study the performance of this estimator when it is used with a cumulative sum (CUSUM) control chart and make direct performance comparisons with the estimated confidence sets obtained from the MLE for step changes. The results show that better confidence can be obtained using the MLE for linear trends when a linear trend disturbance is present. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Estimating the unknown change point in the parameters of the lognormal distribution

ENVIRONMETRICS, Issue 2 2007
V. K. Jandhyala
Abstract We develop change-point methodology for identifying dynamic trends in the parameters of a two-parameter lognormal distribution. The methodology primarily considers the asymptotic distribution of the maximum likelihood estimate of the unknown change point. Among others, the asymptotic distribution enables one to construct confidence interval estimates for the unknown change point. The methodology is applied to identify changes in the monthly water discharges of the Nacetinsky Creek in the German part of the Ergebirge Mountains. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Changes in variance and correlation of soil properties with scale and location: analysis using an adapted maximal overlap discrete wavelet transform

EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2001
R. M. Lark
Summary The magnitude of variation in soil properties can change from place to place, and this lack of stationarity can preclude conventional geostatistical and spectral analysis. In contrast, wavelets and their scaling functions, which take non-zero values only over short intervals and are therefore local, enable us to handle such variation. Wavelets can be used to analyse scale-dependence and spatial changes in the correlation of two variables where the linear model of coregionalization is inadmissible. We have adapted wavelet methods to analyse soil properties with non-stationary variation and covariation in fairly small sets of data, such as we can expect in soil survey, and we have applied them to measurements of pH and the contents of clay and calcium carbonate on a 3-km transect in Central England. Places on the transect where significant changes in the variance of the soil properties occur were identified. The scale-dependence of the correlations of soil properties was investigated by calculating wavelet correlations for each spatial scale. We identified where the covariance of the properties appeared to change and then computed the wavelet correlations on each side of the change point and compared them. The correlation of topsoil and subsoil clay content was found to be uniform along the transect at one important scale, although there were significant changes in the variance. In contrast, carbonate content and pH of the topsoil were correlated only in parts of the transect. [source]

Detection of a possible change point in atmospheric variability in the North Atlantic and its effect on Scandinavian glacier mass balance

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 14 2005
Rowan Fealy
Abstract Climate change, resulting from an increase in global temperatures, is likely to have a large impact on glaciers and glacier meltwater during the course of the present century resulting in significant contributions to sea level rise. Globally, glaciers are in retreat, partly as a response to the return to warmer conditions after the last neoglacial period during the Little Ice Age but also because of the almost continuous increases evident in global temperature since then. In contrast, Scandinavian glaciers, particularly maritime glaciers, were maintaining equilibrium or advancing over the closing decades of the last century possibly resulting from an increased moisture flux over the North Atlantic. While the more continental glaciers were still declining, the rate of decline diminished during the late 1980s. This coincides with an accelerated rate of increase evident on the maritime glaciers in southwestern Norway. A change point in atmospheric variability in the North Atlantic is identified as having occurred during this period. This change point is associated with an intensification of westerlies over Europe, particularly since the late 1980s, which significantly contributes to increases in temperature and precipitation over northern Europe while suppressing the penetration of warm, moist air into more southern European locations. Regional variations in temperature and precipitation from selected Scandinavian stations are also found to be consistent with the changes in the large-scale modes of atmospheric variability in the North Atlantic. Copyright © 2005 Royal Meteorological Society [source]

Trends in extreme daily rainfall across the South Pacific and relationship to the South Pacific Convergence Zone

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 8 2003
G. M. Griffiths
Abstract Daily rainfall records from 22 high-quality stations located in the South Pacific were analysed, over the common period 1961,2000, in order to assess whether extreme rainfall events have altered in their frequency or magnitude. A comprehensive spatial coverage across the South Pacific was provided, analysing a range of indices of extreme precipitation, which reflect both high rainfall events and drought. Clear spatial patterns emerged in the trends of extreme rainfall indices, with a major discontinuity across the diagonal section of the South Pacific Convergence Zone (SPCZ). Stations located between 180 and 155°W exhibit a greater number of significant abrupt changes in extreme climate than elsewhere in the South Pacific, and the majority of climatic jumps occur in the 1970s or 1980s (coincident with a displacement northeastward of the diagonal part of the SPCZ and a large local increase in mean annual temperature). Notably, all significant abrupt changes in an extreme rainfall intensity index occurred in the late 1970s or early 1980s, and in every case the index showed an increase in extremity following the change point, regardless of station location. For the stations located south of the SPCZ, this may also be linked to the observed warming since the 1970s. Significant abrupt changes in mean precipitation were also identified around the mid 1940s, for two longer, century-scale records, which again correspond to a major displacement of the diagonal section of the SPCZ. An indicator of the diagonal SPCZ position is significantly temporally correlated with an extreme rainfall intensity index, at two locations either side of the diagonal section of the SPCZ, at decadal time scales or longer. This suggests that the displacement of the diagonal portion of the SPCZ on decadal time scales influences not only mean precipitation, but also daily rainfall extremes. Copyright © 2003 Royal Meteorological Society [source]

Wavelet change-point estimation for long memory non-parametric random design models

JOURNAL OF TIME SERIES ANALYSIS, Issue 2 2010
Lihong Wang
62G08; 62G05; 62G20 For a random design regression model with long memory design and long memory errors, we consider the problem of detecting a change point for sharp cusp or jump discontinuity in the regression function. Using the wavelet methods, we obtain estimators for the change point, the jump size and the regression function. The strong consistencies of these estimators are given in terms of convergence rates. [source]

On the exponentially weighted moving variance

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 7 2009
Longcheen Huwang
Abstract MacGregor and Harris (J Quality Technol 25 (1993) 106,118) proposed the exponentially weighted mean squared deviation (EWMS) and the exponentially weighted moving variance (EWMV) charts as ways of monitoring process variability. These two charts are particularly useful for individual observations where no estimate of variability is available from replicates. However, the control charts derived by using the approximate distributions of the EWMS and EWMV statistics are difficult to interpret in terms of the average run length (ARL). Furthermore, both control charting schemes are biased procedures. In this article, we propose two new control charts by applying a normal approximation to the distributions of the logarithms of the weighted sum of chi squared random variables, which are respectively functions of the EWMS and EWMV statistics. These new control charts are easy to interpret in terms of the ARL. On the basis of the simulation studies, we demonstrate that the proposed charts are superior to the EWMS and EWMV charts and they both are nearly unbiased for the commonly used smoothing constants. We also compare the performance of the proposed charts with that of the change point (CP) CUSUM chart of Acosta-Mejia (1995). The design of the proposed control charts is discussed. An example is also given to illustrate the applicability of the proposed control charts. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009 [source]

Identifying the time of polynomial drift in the mean of autocorrelated processes

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2010
Marcus B. Perry
Abstract Control charts are used to detect changes in a process. Once a change is detected, knowledge of the change point would simplify the search for and identification of the special ause. Consequently, having an estimate of the process change point following a control chart signal would be useful to process engineers. This paper addresses change point estimation for covariance-stationary autocorrelated processes where the mean drifts deterministically with time. For example, the mean of a chemical process might drift linearly over time as a result of a constant pressure leak. The goal of this paper is to derive and evaluate an MLE for the time of polynomial drift in the mean of autocorrelated processes. It is assumed that the behavior in the process mean over time is adequately modeled by the kth-order polynomial trend model. Further, it is assumed that the autocorrelation structure is adequately modeled by the general (stationary and invertible) mixed autoregressive-moving-average model. The estimator is intended to be applied to data obtained following a genuine control chart signal in efforts to help pinpoint the root cause of process change. Application of the estimator is demonstrated using a simulated data set. The performance of the estimator is evaluated through Monte Carlo simulation studies for the k=1 case and across several processes yielding various levels of positive autocorrelation. Results suggest that the proposed estimator provides process engineers with an accurate and useful estimate for the last sample obtained from the unchanged process. Copyright © 2009 John Wiley & Sons, Ltd. [source]

A clustering approach to identify the time of a step change in Shewhart control charts

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2008
Mehdi Ghazanfari
Abstract Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out-of-control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change-point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase-I and phase-II of normal and non-normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Estimating the Change Point of a Poisson Rate Parameter with a Linear Trend Disturbance

ON THE CHANGE POINT OF THE MEAN RESIDUAL LIFE OF SERIES AND PARALLEL SYSTEMS

Detecting Genomic Aberrations Using Products in a Multiscale Analysis

BIOMETRICS, Issue 3 2010
Xuesong Yu
Summary Genomic instability, such as copy-number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy-number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale-based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change point and adjusted,p -values for the significance of a change point using a step-down maxT permutation algorithm to control the family-wise error rate. A simulation study investigates the finite sample performance of the proposed method and compares it with a more standard sequential testing method. The method is illustrated using two real data sets. [source]

Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death

BIOMETRICS, Issue 1 2010
Binbing Yu
Summary Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal aging. It has been known that cognitive impairment occurs long before the diagnosis of dementia. For individuals who develop dementia, it is important to determine the time when the rate of cognitive decline begins to accelerate and the subsequent gap time to dementia diagnosis. For normal aging individuals, it is also useful to understand the trajectory of cognitive function until their death. A Bayesian change-point model is proposed to fit the trajectory of cognitive function for individuals who develop dementia. In real life, people in older ages are subject to two competing risks, e.g., dementia and dementia-free death. Because the majority of people do not develop dementia, a mixture model is used for survival data with competing risks, which consists of dementia onset time after the change point of cognitive function decline for demented individuals and death time for nondemented individuals. The cognitive trajectories and the survival process are modeled jointly and the parameters are estimated using the Markov chain Monte Carlo method. Using data from the Honolulu Asia Aging Study, we show the trajectories of cognitive function and the effect of education, apolipoprotein E 4 genotype, and hypertension on cognitive decline and the risk of dementia. [source]

Impact of changes in analytical techniques for the measurement of polychlorinated biphenyls and organochlorine pesticides on temporal trends in herring gull eggs

ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 7 2010
Shane R. de Solla
Abstract Changes in analytical approaches during the tenure of monitoring programs for organochlorine (OC) pesticides and polychlorinated biphenyls (PCBs) may affect estimates of temporal trends. We used an in-house reference material to create multiplication factors to adjust the estimates of OC pesticides and PCBs (Aroclor equivalents) in Great Lake herring gull eggs analyzed using electron capture detection (1987,1997) to be more equivalent to estimates using mass spectrometric detection (1998,2005) as well as accompanying differences in analytical procedures. We examined temporal trends in contaminant concentrations in herring gull eggs using change point regressions, to determine whether significant changes in long-term trends were associated with analytical methodology. The highest frequency of change point occurrences shifted from 1997 (when analytical methodology was altered) to 2003 after data adjustment. The explanatory power (r2) of the regressions was lower after adjustment, although only marginally so (mean r2 difference,=,0.04). The initial rates of decline before change points in contaminant concentrations were generally slower after the data adjustment, but after any change points the declines were not significantly different. The regression models did not change for 83.3% of the cases. The effects on the interpretation of long-term temporal trends in herring gull eggs, although not negligible, were minor relative to the magnitude of the temporal changes. Environ. Toxicol. Chem. 2010;29:1476,1483. © 2010 SETAC [source]

Online pattern recognition based on a generalized hidden Markov model for intraoperative vital sign monitoring

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2010
Ping Yang
Abstract The trend patterns of vital signs provide significant insight into the interpretation of intraoperative physiological measurements. We have modeled the trend signal of a vital sign parameter as a generalized hidden Markov model (also known as a hidden semi-Markov model). This model treats a time series as a sequence of predefined patterns and describes the transition between these patterns as a first-order Markov process and the intra-segmental variations as different dynamic linear systems. Based on this model, a switching Kalman smoother combines a Bayesian inference process with a fixed-point Kalman smoother in order to estimate the unconditional true signal values and generates the probability of occurrence for each pattern online. The probabilities of pattern transitions are tested against a threshold to detect change points. A second-order generalized pseudo-Bayesian algorithm is used to summarize the state propagation over time and reduces the computational overhead. The memory complexity is reduced using linked tables. The algorithm was tested on 30 simulated signals and 10 non-invasive-mean-blood-pressure trend signals collected at a local hospital. In the simulated test, the algorithm achieved a high accuracy of signal estimation and pattern recognition. In the test on clinical data, the change directions of 45 trend segments, out of the 54 segments annotated by an expert, were correctly detected with the best performing threshold, and with the introduction of only 8 false-positive detections. The proposed method can detect the changes of trend patterns in a time series online, while generating quantitative evaluation of the significance of detection. This method is promising for physiological monitoring as the method not only generates early alerts, but also summarizes the temporal contextual information for a high-level decision support system. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Bayesian analysis of changes in Radiosonde Atmospheric Temperature

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 5 2009
Christoph Schleip
Abstract This paper describes long-term changes of global atmospheric temperature, using a strict Bayesian approach which considers three different models to describe the time series: the constant model, the linear model and a change point model. The change point model allows the description of nonlinear annual rates of change with associated confidence intervals. We calculate the probabilities of each of the three models and average finally over these models to obtain the expected functional behaviour and rate of change in temperature with annual resolution. We apply this procedure to a new homogenized Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC-A) data set. Annual mean temperature for 13 pressure levels from the surface to 30 hPa is examined. Residual sums of squares reveal that Bayesian-model-averaged function descriptions and rates of changes are especially useful and informative for the surface, troposphere and tropopause and less appropriate for the stratosphere. From the surface up to the tropopause (200,100 hPa), the results reveal that the change point model provides the best data fit. Despite the occurrence of two volcanic eruptions El Chicón (1982) and Mt. Pinatubo (1991), the stratosphere (70,30 hPa) shows a preference for the linear model (60%). The near surface changes exhibit comparatively high change point probability around 1985 and 1995, whereas those at the tropopause level are highest between 1995 and 2000. For the surface and troposphere the model-averaged functional behaviour increases quite constantly, whereas the model-averaged functional behaviour for the tropopause decreases until the end of the 1990s and increases from 2000 onwards. The limitations of the currently used radiosonde data render interpretation of the observed changes difficult. Additionally undetected change points may result from our limited model space. In future it should be tested whether a multiple change point model provides a better data description for the stratosphere. Copyright © 2008 Royal Meteorological Society [source]

Bayesian inference in a piecewise Weibull proportional hazards model with unknown change points

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 4 2007
J. Casellas
Summary The main difference between parametric and non-parametric survival analyses relies on model flexibility. Parametric models have been suggested as preferable because of their lower programming needs although they generally suffer from a reduced flexibility to fit field data. In this sense, parametric survival functions can be redefined as piecewise survival functions whose slopes change at given points. It substantially increases the flexibility of the parametric survival model. Unfortunately, we lack accurate methods to establish a required number of change points and their position within the time space. In this study, a Weibull survival model with a piecewise baseline hazard function was developed, with change points included as unknown parameters in the model. Concretely, a Weibull log-normal animal frailty model was assumed, and it was solved with a Bayesian approach. The required fully conditional posterior distributions were derived. During the sampling process, all the parameters in the model were updated using a Metropolis,Hastings step, with the exception of the genetic variance that was updated with a standard Gibbs sampler. This methodology was tested with simulated data sets, each one analysed through several models with different number of change points. The models were compared with the Deviance Information Criterion, with appealing results. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation data. Moreover, results showed that the piecewise baseline hazard function could appropriately fit survival data, as well as other smooth distributions, with a reduced number of change points. [source]

Bayesian comparison of test-day models under different assumptions of heterogeneity for the residual variance: the change point technique versus arbitrary intervals

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2004
P. López-Romero
Summary Test-day milk yields from Spanish Holstein cows were analysed with two random regression models based on Legendre polynomials under two different assumptions of heterogeneity of residual variance which aim to describe the variability of temporary measurement errors along days in milk with a reduced number of parameters, such as (i) the change point identification technique with two unknown change points and (ii) using 10 arbitrary intervals of residual variance. Both implementations were based on a previous study where the trajectory of the residual variance was estimated using 30 intervals. The change point technique has been previously implemented in the analysis of the heterogeneity of the residual variance in the Spanish population, yet no comparisons with other methods have been reported so far. This study aims to compare the change point technique identification versus the use of arbitrary intervals as two possible techniques to deal with the characterization of the residual variance in random regression test-day models. The Bayes factor and the cross-validation predictive densities were employed for the model assessment. The two model-selecting tools revealed a strong consistency between them. Both specifications for the residual variance were close to each other. The 10 intervals modelling showed a slightly better performance probably because the change point function overestimates the residual variance values at the very early lactation. Zusammenfassung Testtagsgemelke von Spanischen Holstein-Kühen wurden mittels zweier zufälliger Regressionsmodelle, basierend auf Legendre Polynomen, unter zwei unterschiedlichen Voraussetzungen von Heterogenität der Residualvarianz, untersucht, um die Variabilität der Restvarianz der Milchleistung der Testtage durch so wenig Parameter wie möglich beschreiben zu können: 1) dem Verfahren des Wechsel-Identifikationspunktes mit zwei unbekannten Änderungspunkten und 2) der Verwendung von 10 frei gewählten Intervallen der Residualvarianz. Beide Anwendungen beruhen auf einer vorherigen Untersuchung, in der der Verlauf der Residualvarianz durch die Verwendung von 30 Intervallen geschätzt wurde. Das Wechsel-Identifikationspunkt Verfahren wurde bereits bei der Untersuchung der Residualvarianz in der spanischen Population verwendet, aber das Verfahren wurde noch nicht mit anderen Methoden verglichen. Das Ziel dieser Studie war der Vergleich des Wechsel-Identifikationspunkt Verfahrens mit dem Gebrauch von frei wählbaren Intervallen als zwei Möglichkeiten zur Charakterisierung der Residualvarianz in zufälligen Testtags-Regressionsmodellen. Der Bayes'sche Faktor und die Vorhersage der Vergleichsprüfungsdichten wurden zur Bewertung der Modelle verwandt. Beide Verfahren zeigten eine überzeugende Konsistenz der Modelle und die Beschreibung der Residualvarianzen stimmte in beiden Fällen überein. Die Modellierung mit 10 Intervallen zeigte eine etwas bessere Leistung, möglicherweise weil die Wechsel-Identifikationspunkt Funktion die Residualvarianz in der sehr frühen Laktation überbewertet. [source]

Bayesian longitudinal plateau model of adult grip strength

AMERICAN JOURNAL OF HUMAN BIOLOGY, Issue 5 2010
Ramzi W. Nahhas
Objectives: This article illustrates the use of applied Bayesian statistical methods in modeling the trajectory of adult grip strength and in evaluating potential risk factors that may influence that trajectory. Methods: The data consist of from 1 to 11 repeated grip strength measurements from each of 498 men and 533 women age 18,96 years in the Fels Longitudinal Study (Roche AF. 1992. Growth, maturation and body composition: the Fels longitudinal study 1929,1991. Cambridge: Cambridge University Press). In this analysis, the Bayesian framework was particularly useful for fitting a nonlinear mixed effects plateau model with two unknown change points and for the joint modeling of a time-varying covariate. Multiple imputation (MI) was used to handle missing values with posterior inferences appropriately adjusted to account for between-imputation variability. Results: On average, men and women attain peak grip strength at the same age (36 years), women begin to decline in grip strength sooner (age 50 years for women and 56 years for men), and men lose grip strength at a faster rate relative to their peak; there is an increasing secular trend in peak grip strength that is not attributable to concurrent secular trends in body size, and the grip strength trajectory varies with birth weight (men only), smoking (men only), alcohol consumption (men and women), and sports activity (women only). Conclusions: Longitudinal data analysis requires handling not only serial correlation but often also time-varying covariates, missing data, and unknown change points. Bayesian methods, combined with MI, are useful in handling these issues. Am. J. Hum. Biol. 22:648,656, 2010. © 2010 Wiley-Liss, Inc. [source]

A clustering approach to identify the time of a step change in Shewhart control charts

Bayesian estimation of cognitive decline in patients with alzheimer's disease

THE CANADIAN JOURNAL OF STATISTICS, Issue 1 2002
Patrick Béalisle
Abstract Recently, there has been great interest in estimating the decline in cognitive ability in patients with Alzheimer's disease. Measuring decline is not straightforward, since one must consider the choice of scale to measure cognitive ability, possible floor and ceiling effects, between-patient variability, and the unobserved age of onset. The authors demonstrate how to account for the above features by modeling decline in scores on the Mini-Mental State Exam in two different data sets. To this end, they use hierarchical Bayesian models with change points, for which posterior distributions are calculated using the Gibbs sampler. They make comparisons between several such models using both prior and posterior Bayes factors, and compare the results from the models suggested by these two model selection criteria. Estimation bayésienne du déclin cognitif de patients atteints de la maladie d'Alzheimer On s'est beaucoup intéressé ces derniers temps à l'estimation du déclin des fonctions cognitives des personnes atteintes de la maladie d'Alzheimer. Il n'est pas facile de quantifier ce déclin, qui dépend de l'échelle utilisée pour mesurer les fonctions cognitives, mais aussi de la variabilité entre les individus, de 1'incertitude entourant le moment exact du début de leur maladie et d'éventuels effets plancher et plafond. Les auteurs montrent comment il est possible de tenir compte de ces différents éléments en modélisant le déclin observé dans les résultats obtenus par deux groupes de patients au mini-examen de l'état mental. Ils utilisent pour ce faire des modèles bayésiens hiérarchiques avec points de jonction, pour lesquels ils calculent les lois a posteriori au moyen de l'échantillonneur de Gibbs. Ils comparent plusieurs modèles de ce type au moyen de facteurs de Bayes a priori et a posteriori; ils comparent ensuite les résultats des modèles suggérés par ces deux critères de sélection. [source]

Distinguishing between trend-break models: method and empirical evidence

THE ECONOMETRICS JOURNAL, Issue 2 2001
Chih-Chiang Hsu
We demonstrate that in time trend models, the likelihood-based tests of partial parameter stability have size distortions and cannot be applied to detect the changing parameter. A two-step procedure is then proposed to distinguish between different trend-break models. This procedure involves consistent estimation of break dates and properly-sized tests for changing coefficient. In the empirical study of the Nelson-Plosser data set, we find that the estimated change points and trend-break specifications resulting from the proposed procedure are quite different from those of Perron (1989, 1997), Chu and White (1992), and Zivot and Andrews (1992). In another application, our procedure provides formal support for the conclusion of Ben-David and Papell (1995) that real per capita GDPs of most OECD countries exhibit a slope change in trend. [source]

Effectiveness of neural networks to regression with structural changes

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2002
Miyoko Asano
Abstract This paper reports simple numerical experiments of the application of multi-layered and feed-forward neural networks to regression with change points to clarify one of the effectiveness of the neural network model compared with non-parametric regression methods based on scatter plot smoothing. We also show an illustrative example, which successfully draws too rapid growth of GDP in Japan at the bubble economy around 1990 by interpreting decomposition of regression function suggested by the optimal neural networks fitting. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Trends and abrupt changes of precipitation maxima in the Pearl River basin, China

ATMOSPHERIC SCIENCE LETTERS, Issue 2 2009
Q. Zhang
Abstract We applied the Mann-Kendall (MK) test and Bayesian model to systematically explore trends and abrupt changes of the precipitation series in the Pearl River basin. The results showed that no significant trends were detected for annual precipitation and summer or winter precipitation totals. Significant negative trends were identified for the number of rainy days across the Pearl River basin; significant positive trends were observed regarding precipitation intensity (PI). In particular, the precipitation totals and frequencies of extremely high precipitation events are subject to significant positive trends. In addition, the number of extremely low precipitation events was also increasing significantly. Factors affecting the changes in precipitation patterns are the weakening Asian monsoon and consequently increasing moisture transport to Southern China and the Pearl River basin. In summary, the main findings of this study are: (1) increased precipitation variability and high-intensity rainfall was observed though rainy days and low-intensity rainfall have decreased, and (2) the amount of rainfall has changed little but its variability has increased over the time interval divided by change points. These finds indicate potentially increased risk for both agriculture and in locations subject to flooding, both urban and rural, across the Pearl River basin. Copyright © 2009 Royal Meteorological Society [source]