A multiple window scan statistic for time series models

In this article we extend the results derived for scan statistics in Wang and Glaz (2014) for independent normal observations. We investigate the performance of two approximations for the distribution of fixed window scan statistics for time series models. An R algorithm for computing multivariate normal probabilities established in Genz and Bretz (2009) can be used along with proposed approximations to implement fixed window scan statistics for ARMA models. The accuracy of these approximations is investigated via simulation. Moreover, a multiple window scan statistic is defined for detecting a local change in the mean of a Gaussian white noise component in ARMA models, when the appropriate length of the scanning window is unknown. Based on the numerical results, for power comparisons of the scan statistics, we can conclude that when the window size of a local change is unknown, the multiple window scan statistic outperforms the fixed window scan statistics.     

Multiple Window Scan Statistics for Two Dimensional Poisson Processes

In this article, approximations for the distribution of multiple window scan statistics for Poisson Processes on a two dimensional rectangular region are derived, for the conditional and unconditional model. These multiple window scan statistics are based on the minimum of p-values and repeated minimum p-values of fixed window scan statistics. Numerical results are presented to evaluate the performance of these multiple window scan statistics and compare their power with fixed window scan statistics for selected local type alternatives.     

Off-Line Testing for a Changed Segment in the Sample Variance

In this paper, we introduce and investigate a new class of test statistics for the model of a changed segment in the sample variance. We derive the asymptotic distribution of the test statistic under the null hypothesis of no change and its consistency under the changed segment alternative. The consistent procedures of estimating the length and beginning of a changed segment are proposed. Monte-Carlo simulations support theoretical results.__________Published in Lietuvos Matematikos Rinkinys, Vol. 45, No. 2, pp. 241–260, April–June, 2005.     

随机设计下非参数回归模型方差变点Ratio检验

研究随机设计下非参数回归模型方差变点Ratio检验.首先用局部多项式方法估计回归曲线得到残差序列,其次基于残差的平方序列构造Ratio检验统计量并推导检验统计量的极限分布.最后数值模拟与实例分析结果表明方法的有效性.     

Compact Matrix Expressions for Generalized Wald Tests of Equality of Moment Vectors

Asymptotic chi-squared test statistics for testing the equality of moment vectors are developed. The test statistics proposed are generalized Wald test statistics that specialize for different settings by inserting an appropriate asymptotic variance matrix of sample moments. Scaled test statistics are also considered for dealing with nonstandard conditions. The specialization will be carried out for testing the equality of multinomial populations, and the equality of variance and correlation matrices for both normal and nonnormal data. When testing the equality of correlation matrices, a scaled version of the normal theory chi-squared statistic is proven to be an asymptotically exact chi-squared statistic in the case of elliptical data.     

Scan Statistics for Normal Data with Outliers

Methodology and Computing in Applied Probability - In this article we investigate the performance of scan statistics based on moving medians, as test statistics for detecting a local change in...     

Testing for changes in the mean or variance of long memory processes

In this paper, we study the asymptotic CUSUM tests for detecting changes in the mean or variance of a moving-average process with long memory. When there is no change over [O,T], the asymptotic distribution of the test statistic is derived, which allows us to find asymptotic critical values. When there is a change, the behavior of the test statistic is discussed. Conditions for the consistency of these tests are also discussed. Based on the asymptotic results, simulation studies of testing for changes in the mean show that the CUSUM test proposed performs well.     

Multiple Window and Cluster Size Scan Procedures

Researchers apply scan statistics to test for unusually large clusters of events within a time window of specified length w, or alternatively an unusually small window w that contains a specified number of events. In some cases, the researcher is interested in testing for a range of specified window lengths, or a set of several specified number of events k (cluster sizes). In this paper, we derive accurate approximations for the joint distributions of scan statistics for a range of values of w, or of k, that can be used to set an experiment-wide level of significance that takes into account the multiple comparisons involved. We use these methods to compare different ways of choosing the window sizes for the different cluster sizes. One special case is a multiple comparison procedure based on a generalized likelihood ratio test (GLRT) for a range of window sizes. We compare the power of the GLRT with another method for allocating the window sizes. We find that the GLRT is sensitive for very small window sizes at the expense of moderate and larger window sizes. We illustrate these results on two examples, one involving clustering of translocation breakpoints in DNA, and the other involving disease clusters.     

Log-likelihood ratio test for detecting transient change

The limit behaviour of scan statistics in a form of maxima of moving sum processes with changing window size for detecting a transient change is studied. Approximate critical values obtained from tail behaviour of the limit variables that are maxima of some locally homogeneous Gaussian fields are compared with critical values obtained by simulation.     

非线性均值方差模型的贝叶斯数据删除统计诊断

文章在非线性均值方差模型框架下基于K-L距离研究贝叶斯数据删除影响的统计诊断问题,通过应用Gibbs抽样和MH算法估计贝叶斯数据删除影响诊断统计量.随机模拟研究和红鳟鲑鱼数据的数值例子说明该诊断方法的可行性.     

On the anomalous behaviour of a class of locality statistics

A scan statistic methodology for detecting anomalies has been developed for application to graphs, where “anomalies” are equated with vertices that exhibit distinctive local connectivity properties. We present an “anomaly graph” construction that illustrates the capabilities of these scan statistics via the behaviour of their associated locality statistics on our anomaly graphs.     

Estimating the Distributions of Scan Statistics with High Precision

In many statistical applications one is concerned with the estimation of the distribution of the maximum or minimum number of points in a moving window of fixed length, called scan statistics. Scan statistics are also extremes of 1-dependent sequences. A result of Haiman (1999) provides approximations of these distributions together with sharp bounds for the corresponding errors. Applications concern the maximum cluster of points on a line or on a circle and multiple coverage by subintervals or subarcs of fixed size. We compare our method with some existing empirical and non empirical methods and show how it can be applied to multidimensional scanning     

Scan Statistic Tail Probability Assessment Based on Process Covariance and Window Size

A scan statistic is examined for the purpose of testing the existence of a global peak in a random process with dependent variables of any distribution. The scan statistic tail probability is obtained based on the covariance of the moving sums process, thereby accounting for the spatial nature of the data as well as the size of the searching window. Exact formulas linking this covariance to the window size and the correlation coefficient are developed under general, common and auto covariance structures of the variables in the original process. The implementation and applicability of the formulas are demonstrated on multiple processes of t-statistics, treating also the case of unknown covariance. A sensitivity analysis provides further insight into the variant interaction of the tail probability with the influence parameters. An R code for the tail probability computation and the data analysis is offered within the supplementary material.     

Computing Scan Statistic p Values Using Importance Sampling,With Applications to Genetics and Medical Image Analysis

We present an importance sampling method for deciding, based on an observed random field, if a scan statistic provides significant evidence of increased activity in some localized region of time or space. Our method allows consideration of scan statistics based simultaneously on multiple scan geometries. Our approach yields an unbiased p value estimate whose variance is typically smaller than that of the naive hit-or-miss Monte Carlo technique when the p value is small. Furthermore, our p value estimate is often accurate for critical values that are not far enough in the tails of the null distribution to allow for accurate approximations via extreme value theory. The importance sampling approach unifies the analysis of various random field models, from (spatial) point processes to Gaussian random fields. For a scan statistic M, the method produces a p value of the form P[M ≥ τ] = Bρ, where B is the Bonferroni upper bound and the correction factor ρ measures the conservativeness of this upper bound. We present the application of our importance sampling estimator to multinomial sequences (molecular genetics), spatial point processes (digital mammography), and Gaussian random fields (PET scan brain imagery).     

控制过程方差的CUSUMQ图及其性质

崔恒建．控制过程方差的ＣＵＳＵＭＱ图及其性质．数理统计与管理，１９９８，１７（４），３３～３８．Ｑｕｓｅｎｂｅｒｒｙ（１９９５）基于样本方差的标准化变换Φ－１［Ｈｎ－１（（ｎ－１）Ｓ２／σ２０）］提出了控制过程方差的累积和（ＣＵＳＵＭ）Ｑ控制图。本文我们描述了在控制过程方差变化中这种ＣＵＳＵＭＱ控制图的性质，并将控制图的设计方法用到单边及双边的ＣＵＳＵＭＱ图，说明它几乎是最优的。而且我们发现在控制过程方差的微小变化时，设计的ＣＵＳＵＭＱ图的性能要优于基于ｌｏｇ（Ｓ２）的ＣＵＳＵＭ和ＥＷＭＡ图     

Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters

Spatial scan statistics are commonly used for geographic disease cluster detection and evaluation. We propose and implement a modified version of the simulated annealing spatial scan statistic that incorporates the concept of “non-compactness” in order to penalize clusters that are very irregular in shape. We evaluate its power for the simulated annealing scan and compare it with the circular and elliptic spatial scan statistics. We observe that, with the non-compactness penalty, the simulated annealing method is competitive with the circular and elliptic scan statistic, and both have good power performance. The elliptic scan statistic is computationally faster and is well suited for mildly irregular clusters, but the simulated annealing method deals better with highly irregular cluster shapes. The new method is applied to breast cancer mortality data from northeastern United States.     

基于加总和乘积的方差分解法及其应用

研究了加总式和乘积式的方差分解问题,证明了在因变量等于各自变量之和的条件下,因变量方差等于各自变量与因变量的协方差之和;在因变量等于各自变量之乘积的条件下,因变量对数值的方差等于各自变量对数值与因变量对数值的协方差之和．以中国31个省份2005－2012年的居民人均收入及其影响因素的统计数据资料为例,说明了加总式和乘积式的方差分解法的具体应用．     

Symmetrized approximate score rank tests for the two-sample case

Rank test statistics for the two-sample problem are based on the sum of the rank scores from either sample. However, a critical difference can occur when approximate scores are used since the sum of the rank scores from sample 1 is not equal to minus the sum of the rank scores from sample 2. By centering and scaling as described in Hajek and Sidak (1967, Theory of Rank Tests, Academic Press, New York) for the uncensored data case the statistics computed from each sample become identical. However such symmetrized approximate scores rank statistics have not been proposed in the censored data case. We propose a statistic that treats the two approximate scores rank statistics in a symmetric manner. Under equal censoring distributions the symmetric rank tests are efficient when the score function corresponds to the underlying model distribution. For unequal censoring distributions we derive a useable expression for the asymptotic variance of our symmetric rank statistics.     

Measures of influence for the functional linear model with scalar response

This paper studies how to identify influential observations in the functional linear model in which the predictor is functional and the response is scalar. Measurement of the effects of a single observation on estimation and prediction when the model is estimated by the principal components method is undertaken. For that, three statistics are introduced for measuring the influence of each observation on estimation and prediction of the functional linear model with scalar response that are generalizations of the measures proposed for the standard regression model by [D.R. Cook, Detection of influential observations in linear regression, Technometrics 19 (1977) 15-18; D. Peña, A new statistic for influence in linear regression, Technometrics 47 (2005) 1-12] respectively. A smoothed bootstrap method is proposed to estimate the quantiles of the influence measures, which allows us to point out which observations have the larger influence on estimation and prediction. The behavior of the three statistics and the quantile estimation bootstrap based method is analyzed via a simulation study. Finally, the practical use of the proposed statistics is illustrated by the analysis of a real data example, which show that the proposed measures are useful for detecting heterogeneity in the functional linear model with scalar response.     

Inference for Spatial Autoregressive Models with Infinite Variance Noises

A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a stable law with index of stability α, α ∈ (0, 2]. It is shown that when the model is stationary, the self-weighted quantile estimate of the parameter has a closed form and converges to a normal limiting distribution, which avoids the difficulty of Roknossadati and Zarepour (2010) in deriving their limiting distribution for an M-estimate. On the contrary, we show that when the model is not stationary, the proposed estimates have the same limiting distributions as those of Roknossadati and Zarepour. Furthermore, a Wald test statistic is proposed to consider the test for a linear restriction on the parameter, and it is shown that under a local alternative, the Wald statistic has a non-central chisquared distribution. Simulations and a real data example are also reported to assess the performance of the proposed method.