The growth curve model with an autoregressive covariance structure

The growth curve model with an autoregressive covariance structure is considered. An iterative algorithm for finding the MLE's of the parameters in the model is presented, based on the modified likelihood equations. Asymptotic distributions of the MLE's are obtained when the sample size is large. A likelihood ratio statistic for testing the autoregressive covariance structure is presented.     

Testing for elliptical symmetry in covariance-matrix-based analyses

Many normal-theory test procedures for covariance matrices remain valid outside the family of normal distributions if the matrix of fourth-order moments has structure similar to that of a normal distribution. In particular, for elliptical distributions this matrix of fourth-order moments is a scalar multiple of that for the normal, and for this reason many normal-theory statistics can be adjusted by a scalar multiple so as to retain their asymptotic distributional properties across elliptical distributions. For these analyses, a test for the validity of these scalar-adjusted normal-theory procedures can be viewed as a test on the structure of the matrix of fourth-order moments. In this paper, we develop a Wald statistic for conducting such a test.     

Properties of residuals for spatial point processes

For any point process in that has a Papangelou conditional intensity λ, we define a random measure of ‘innovations’ which has mean zero. When the point process model parameters are estimated from data, there is an analogous random measure of ‘residuals’. We analyse properties of the innovations and residuals, including first and second moments, conditional independence, a martingale property, and lack of correlation. Some large sample asymptotics are studied. We derive the marginal distribution of smoothed residuals by solving a distributional equivalence.     

Penalized Fast Subset Scanning

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic. However, PFSS also allows incorporation of prior information about each data element’s probability of inclusion, which was not previously possible within the subset scan framework. PFSS builds on two main results: first, we prove that a large class of likelihood ratio statistics satisfy a property that allows additional, element-specific penalty terms to be included while maintaining efficient computation. Second, we prove that the penalized statistic can be maximized exactly by evaluating only O(N) subsets. As a concrete example of the PFSS framework, we incorporate “soft” constraints on spatial proximity into the spatial event detection task, enabling more accurate detection of irregularly shaped spatial clusters of varying sparsity. To do so, we develop a distance-based penalty function that rewards spatial compactness and penalizes spatially dispersed clusters. This approach was evaluated on the task of detecting simulated anthrax bio-attacks, using real-world Emergency Department data from a major U.S. city. PFSS demonstrated increased detection power and spatial accuracy as compared to competing methods while maintaining efficient computation.     

Bargraph statistics on words and set partitions

In this paper, we consider statistics on partitions of an n-element set represented as a subset of the bargraphs that have n horizontal steps. More precisely, we find the joint distribution of the area and up step statistics on the latter subset of bargraphs, thereby obtaining new refined counts on partitions having a fixed number of blocks. Furthermore, we give explicit formulas in terms of the Stirling numbers for the total area and number of up steps in bargraphs corresponding to partitions, providing both algebraic and combinatorial proofs. Finally, we find asymptotic estimates for the average and total values of these statistics and as a consequence obtain some new identities for the Stirling and Bell numbers.     

Global test for metabolic pathway differences between conditions

In many metabolomics applications there is a need to compare metabolite levels between different conditions, e.g., case versus control. There exist many statistical methods to perform such comparisons but only few of these explicitly take into account the fact that metabolites are connected in pathways or modules. Such a priori information on pathway structure can alleviate problems in, e.g., testing on individual metabolite level. In gene-expression analysis, Goeman's global test is used to this extent to determine whether a group of genes has a different expression pattern under changed conditions. We examined if this test can be generalized to metabolomics data. The goal is to determine if the behavior of a group of metabolites, belonging to the same pathway, is significantly related to a particular outcome of interest, e.g., case/control or environmental conditions. The results show that the global test can indeed be used in such situations. This is illustrated with extensive intracellular metabolomics data from Escherichia coli and Saccharomyces cerevisiae under different environmental conditions.     

Developing a new nonbinary SNP fluorescent multiplex detection system for forensic application in China

Nonbinary single‐nucleotide polymorphisms (SNPs) are potential forensic genetic markers because their discrimination power is greater than that of normal binary SNPs, and that they can detect highly degraded samples. We previously developed a nonbinary SNP multiplex typing assay. In this study, we selected additional 20 nonbinary SNPs from the NCBI SNP database and verified them through pyrosequencing. These 20 nonbinary SNPs were analyzed using the fluorescent‐labeled SNaPshot multiplex SNP typing method. The allele frequencies and genetic parameters of these 20 nonbinary SNPs were determined among 314 unrelated individuals from Han populations from China. The total power of discrimination was 0.9999999999994, and the cumulative probability of exclusion was 0.9986. Moreover, the result of the combination of this 20 nonbinary SNP assay with the 20 nonbinary SNP assay we previously developed demonstrated that the cumulative probability of exclusion of the 40 nonbinary SNPs was 0.999991 and that no significant linkage disequilibrium was observed in all 40 nonbinary SNPs. Thus, we concluded that this new system consisting of new 20 nonbinary SNPs could provide highly informative polymorphic data which would be further used in forensic application and would serve as a potentially valuable supplement to forensic DNA analysis.     

一类具多个偏差变元Rayleigh型p-Laplacian方程周期解（英文）

利用Manasevich-Mawhin延拓定理和一些分析技巧,建立了一类具多个偏差变元Rayleigh型p-Laplacian方程周期解存在的充分性条件,是已有结果的一个补充.     

关于两种检验统计量为单峰分布时的最佳双边检验

根据检验统计量的不同在两种情况下证明了单峰分布的最佳双边检验是存在且唯一的,同时给出了求参数最佳双边检验需满足的条件;并且将最佳双边检验与传统检验进行了比较．最后讨论了最佳双边检验与最短区间估计的关系．