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The study of long‐term precipitation record is critically important for a country, whose food security and economy rely on the timely availability of water. In this study, the historical 102‐year (1901–2002) rainfall data of the Sindh River basin (SRB), India, were analyzed for seasonal and annual trends. The Mann–Kendall test and Sen's slope model were used to identify the trend and the magnitude of the change, respectively. Spatial interpolation technique such as Kriging was used for interpolating the spatial pattern over SRB in GIS environment. The analysis revealed the significantly increasing precipitation trend in both seasonal and annual rainfall in the span of 102 years.

Background: The reports of numerous outbreaks of whiteflies from different parts of the world have increased its medical importance. The aim of this study was to determine relationship between environmental changes and climatic factors with the outbreak of the whitefly population in Tehran, the capital of Iran. Methods: This study was carried out in urban areas of Tehran, where the increasing population of whiteflies was re­ported frequently during 2018. In order to entrap the whiteflies, 20 yellow sticky cards smeared with white refined grease were installed on the trunks of the trees at twice per month as trapping time intervals. The captured flies were transferred and conserved in cans containing 70% alcohol and were counted accurately under a stereomicroscope. To determine the relationship between air quality index, precipitation, air temperature and air humidity as environmental and climatic factors with the abundance of whiteflies, change point analysis and Generalized Estimating Equations (GEE) was used. Results: The most density of white flies per trap was 256.6 and 155.6 in early October and late September respectively. The number moved closer to zero from November to April. The population of whiteflies was inversely correlated with the level of air quality index (p= 0.99) and precipitation (p= 0.95), and it had a direct correlation with the high temper­ature. Also, the population of whiteflies had a direct correlation with the level of air humidity in the first half of the year Conclusion: According to these findings, during spring and summer from early May to early October.  

We propose an approximation to the mean of the ratio of cumulative sums of a sequence of independently distributed nonnegative random variables. Both leading term and higher-order saddlepoint approximations are presented, and the results are applied to a vector ratio approximation, change-point analysis, and kernel regression. The Canadian Journal of Statistics 42: 325-336; 2014 © 2014 Statistical Society of Canada Les auteurs proposent une approximation pour la moyenne du ratio de la somme cumulative d'une séquence de variables aléatoires non négatives indépendantes. Des approximations en point de selle sont présentées pour le terme principal et les termes d'ordre supérieur. Les résultats sont appliqués à la distribution d'un vecteur de ratios, à une analyse du point de rupture et à la régression à noyau. La revue canadienne de statistique 42: 325-336; 2014 © 2014 Société statistique du Canada

Methodology is proposed to uncover structural breaks in functional data that is ‘fully functional’ in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional procedures perform best under conditions when analogous estimators based on functional principal component analysis are at their worst, namely when the feature of interest is orthogonal to the leading principal components of the data. The theoretical findings are confirmed by means of a Monte Carlo simulation study in finite samples. An application to annual temperature curves illustrates the practical relevance of the procedures proposed.

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method's ability to observe how the networks between different brain regions changed with subjects' emotional state. ► This paper introduces Dynamic Connectivity Regression (DCR). ► DCR allows for the study of dynamic changes in brain connectivity. ► DCR detects temporal change points in connectivity. ► DCR estimates a graph in the interval that falls between pairs of change points.

Land use and climate dynamics have a pronounced impact on water resources, biodiversity, land degradation, and productivity at all scales. Thus, in this study, we present the spatio-temporal dynamics of land use change and climate aiming to provide a scientific evidence about gains and losses in major land use categories and associated drivers and significancy and homogeneity of climate change. To this end, Landsat images and historical climate data have been used to determine the dynamics. In addition, population census data and land use policy have been considered to assess the potential drivers of land use change. The spatio-temporal land use dynamics have been evaluated using transition matrix and dynamics index. Likewise, shifts in the climate data were analyzed using change point analysis and three homogenous climate zones have been identified using principal component analysis. The results show that, from 1989 to 2019, the areal percentage of agricultural land increased by 27.5%, settlement by 0.8%, and barren land 0.4% while the natural vegetation, wetland, water body, and grass land decreased by 24.5%, 1.6%, 0.5%, and 2.1%, respectively. The land use dynamics have been stronger in the first decade of the study period. An abrupt shift of climate has occurred in the 1980s. In the last four decades, rainfall shows a not significant decreasing trend. However, a significant increasing trend has been observed for temperature. Rapid population growth, agricultural expansion policy, and climate variability have been identified as the underlying drivers of land use dynamics.

Using change point analysis, this study showed that even after long-term successful antiretroviral therapy in human immunodeficiency-infected patients with suppressed viremia, the CD4 cell count, CD4 percentage, and CD4/CD8 ratio did not recover to levels seen in healthy individuals. Abstract Background The extent and duration of long-term recovery of CD4 count, CD4 percentage (CD4%), and CD4/CD8 ratio after initiation of combination antiretroviral therapy (cART) in patients with a suppressed viral load (VL) are largely unknown. Methods Patients infected with human immunodeficiency virus type 1 who started cART between January 2004 and January 2012 and showed persistent viral suppression (VL, <200 copies/mL) for ≥4 years were followed up at the AIDS Clinical Center in Tokyo. Change point analysis was used to determine the time point when CD4 count recovery shows a plateau, and a linear mixed model was applied to estimate the CD4 count at this change point. Results Data were analyzed from 752 patients (93% male; median age, 38 years; median baseline CD4 cell count, 172/µL [interquartile range CD4%, 13.8%]; CD4/CD8 ratio, 0.23). The median follow-up period was 81.2 months, and 91 patients (12.1%) were followed up for >10 years. Change point analysis showed that CD4 count, CD4%, and CD4/CD8 ratio continued to increase until 78.6, 62.2, and 64.3 months, respectively, with adjusted means of 590/µL (95% confidence interval, 29.5%, and 0.89, respectively, at the change point. Although CD4 counts ≥500/μL were achieved in 73.8% of the study patients, they were not achieved in 48.2% of those with a baseline CD4 count <100/μL. Neither the CD4% nor the CD4/CD8 ratio were normalized in a majority of patients. Conclusions The results showed lack of normalization of CD4 count, CD4%, and CD4/CD8 ratio to the levels seen in healthy individuals even after long-term successful cART in patients with a suppressed VL.

Consider d dependent change point tests, each based on a CUSUM-statistic. We provide an asymptotic theory that allows us to deal with the maximum over all test statistics as both the sample size n and d tend to infinity. We achieve this either by a consistent bootstrap or an appropriate limit distribution. This allows for the construction of simultaneous confidence bands for dependent change point tests, and explicitly allows us to determine the location of the change both in time and coordinates in high-dimensional time series. If the underlying data has sample size greater or equal n for each test, our conditions explicitly allow for the large d small n situation, that is, where n/d → 0. The setup for the high-dimensional time series is based on a general weak dependence concept. The conditions are very flexible and include many popular multivariate linear and nonlinear models from the literature, such as ARMA, GARCH and related models. The construction of the tests is completely nonparametric, difficulties associated with parametric model selection, model fitting and parameter estimation are avoided. Among other things, the limit distribution for max1≤h≤d sup0≤t≤1 |Wt,h - tW1,h| is established, where {Wt,h}1≤h≤d denotes a sequence of dependent Brownian motions. As an application, we analyze all S&P 500 companies over a period of one year.

Classical change point analysis aims at (1) detecting abrupt changes in the mean of a possibly nonstationary time series and at (2) identifying regions where the mean exhibits a piecewise constant behavior. In many applications however, it is more reasonable to assume that the mean changes gradually in a smooth way. Those gradual changes may either be nonrelevant (i.e., small), or relevant for a specific problem at hand, and the present paper presents statistical methodology to detect the latter. More precisely, we consider the common nonparametric regression model Xi = μ(i/n) + ε i with centered errors and propose a test for the null hypothesis that the maximum absolute deviation of the regression function μ from a functional g(μ) (such as the value μ(0) or the integral ∫ 0 1 μ ( t ) d t ) is smaller than a given threshold on a given interval [x 0, x 1] ⊆ [0, 1]. A test for this type of hypotheses is developed using an appropriate estimator, say d̂ ∞, n , for the maximum deviation d ∞ = sup t ∈ [ x 0 , x 1 ] | μ ( t ) − g ( μ ) | . We derive the limiting distribution of an appropriately standardized version of d̂ ∞, n , where the standardization depends on the Lebesgue measure of the set of extremal points of the function μ(·) − g(μ). A refined procedure based on an estimate of this set is developed and its consistency is proved. The results are illustrated by means of a simulation study and a data example.

A restrictive assumption in change point analysis is “stationarity under the null hypothesis of no change-point”, which is crucial for asymptotic theory but not very realistic from a practical point of view. For example, if change point analysis for correlations is performed, it is not necessarily clear that the mean, marginal variance or higher order moments are constant, even if there is no change in the correlation. This paper develops change point analysis for the correlation structures under less restrictive assumptions. In contrast to previous work, our approach does not require that the mean, variance and fourth order joint cumulants are constant under the null hypothesis. Moreover, we also address the problem of detecting relevant change points.