Test on Stochastic Block Model: Local Smoothing and Extreme Value Theory

In this paper, to obtain a consistent estimator of the number of communities, the authors present a new sequential testing procedure, based on the locally smoothed adjacency matrix and the extreme value theory. Under the null hypothesis, the test statistic converges to the type I extreme value distribution, and otherwise, it explodes fast and the divergence rate could even reach n in the strong signal case where n is the size of the network, guaranteeing high detection power. This method is simple to use and serves as an alternative approach to the novel one in Lei (2016) using random matrix theory. To detect the change of the community structure, the authors also propose a two-sample test for the stochastic block model with two observed adjacency matrices. Simulation studies justify the theory. The authors apply the proposed method to the political blog data set and find reasonable group structures.

     

Testing serial correlation in semiparametric varying coefficient partially linear errors-in-variables model

The authors propose a V _{N, p} test statistic for testing finite-order serial correlation in a semiparametric varying coefficient partially linear errors-in-variables model. The test statistic is shown to have asymptotic normal distribution under the null hypothesis of no serial correlation. Some Monte Carlo experiments are conducted to examine the finite sample performance of the proposed V _{N, p} test statistic. Simulation results confirm that the proposed test performs satisfactorily in estimated size and power. This research is supported by the National Natural Science Foundation of China under Grant Nos. 10871217 and 40574003; the Science and Technology Project of Chongqing Education Committee under Grant No. KJ080609; the Doctor's Start-up Research Fund under Grant No. 08-52204; and the Youth Science Research Fund of Chongqing Technology and Business University under Grant No. 0852008.     

A new procedure for testing normality based on the L 2 Wasserstein distance

This paper proposes a new goodness-of-fit test for normality based on the L ₂ Wasserstein distance. The authors first construct a probability through the Bootstrap resampling. Although the probability is not distributed uniformly on the interval (0, 1) under the null hypothesis, it is shown that its distribution is free from the unknown parameters, which indicates that such a probability can be taken as the test statistic. It emerges from the simulation study of power that the new test is able to better discriminate between the normal distribution and those distributions with short tails. For such alternatives, it has a substantially better power than existing tests including the Anderson-Darling test and Shapiro-Wilk test, which are two of the best tests for normality. In addition, the sensitivity analysis of tests is also investigated in the presence of moderate perturbation, which shows that the new test is a rather robust test.     

Test for Heteroscedasticity in Partially Linear Regression Models

Testing heteroscedasticity determines whether the regression model can predict the dependent variable consistently across all values of the explanatory variables. Since the proposed tests could not detect heteroscedasticity in all cases, more precisely in heavy-tailed distributions, the authors established new comprehensive test statistic based on Levene's test. The authors built the asymptotic normality of the test statistic under the null hypothesis of homoscedasticity based on the recent theory of analysis of variance for the infinite factors level. The proposed test uses the residuals from a regression model fit of the mean function with Levene's test to assess homogeneity of variance. Simulation studies show that our test yields better than other methods in almost all cases even if the variance is a nonlinear function. Finally, the proposed method is implemented through a real data-set.     

Consistency of Chi-Squared Test with Varying Number of Classes

The classical chi-squared goodness of fit test assumes the number of classes is fixed,meanwhile the test statistic has a limiting chi-square distribution under the null hypothesis.It is well known that the number of classes varying with sample size in the test has attached more and more attention.However,in this situation,there is not theoretical results for the asymptotic property of such chi-squared test statistic.This paper proves the consistency of chi-squared test with varying number of classes under some conditions.Meanwhile,the authors also give a convergence rate of KolmogorovSimirnov distance between the test statistic and corresponding chi-square distributed random variable.In addition,a real example and simulation results validate the reasonability of theoretical result and the superiority of chi-squared test with varying number of classes.     

A nonparametric test against an umbrella alternative for stratified data

Trend test in dose-response has been a central problem in medicine. This paper treats the problem of comparing umbrella pattern treatment effects. Under an ordered m × r × k table, this article considers testing the hypothesis that all multinomial populations are conditional independence against the alternative that they are in an umbrella trend. For this hypothesis test problem, this article introduces a model-free test method by using likelihood ratio test statistic and gives the asymptotic distribution of the test statistic. Simulation study is conducted to compare the empirical power performed via the proposed method and others. Finally, two real data are studied to illustrate the validity of the proposed method.     

THE ESTIMATE AND TEST IN THE SYMMETRICAUXILIARY INFORMATION

1.IntroductionLetX,XI,X2,''lXui.i.d.~FO.TheempiricaldistributionFn=Z::,6x./niswellknowntobethenonparametricmaximumlikelihoodestimateofFObasedonXI,X2,'')Xu.Here6:denotesapointmassatx.ThelikelihoodfunctionthatFimaximizesisL(G)=fi:=,G{xi},whereG{xi}istheprobabilityoftheset{xi}underG,xiistheobservedvalueofXiandGisanyprobabilitymeasureonCP.WedefinetheempiricallikelihoodfunctionnL(G)=fiG{xi},whereG<相似文献   

Testing inequality constraints in a linear regression model with spherically symmetric disturbances

This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances, and derive the distributions of the test statistic under null and nonnull hypotheses. The power of the test is then discussed. Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t （Mt） distribution.     

Positivity and Stability of Fractional-Order Linear Time-Delay Systems

This article focuses on the positivity and the asymptotic stability of fractional-order linear time-delay systems (FOLTDSs) which are composed of N (N ≥ 2) subsystems. Firstly, a sufficient and necessary condition is given to ensure the positivity of FOLTDSs. The solutions of the studied systems are obtained by using the Laplace transform method, and it can be observed that the positivity of FOLTDSs is completely determined by the series of matrices and independent of the magnitude of time-delays. Secondly, a theorem is given to prove the asymptotic stability of positive FOLTDSs. By considering the monotonicity and asymptotic properties of systems with constant time-delay, it is further shown that the asymptotic stability of positive FOLTDSs is independent of the time-delay. Next, a state-feedback controller, whose gain matrix is derived by resolving a linear programming question, is designed such that the state variables of the systems are nonnegative and asymptotically convergent. When the order of the FOLTDSs is greater than one, by utilizing a proposed property of Caputo derivative, a sufficient condition for the positivity of FOLTDS is presented. Finally, simulation examples are presented to verify the validity and practicability of the theoretical analysis.

     

Outlier Detection via a Block Diagonal Product Estimator

Outlier detection is a fundamental topic in robust statistics. Traditional outlier detection methods try to find a clean subset of given size, which is used to estimate the location vector and scatter matrix, and the outliers can be flagged by the Mahalanobis distance. However, methods such as the minimum covariance determinant approach cannot be applied directly to high-dimensional data, especially when the dimension of the sample is greater than the sample size. A novel fast detection procedure based on a block diagonal partition is proposed, and the asymptotic distribution of the modified Mahalanobis distance is obtained. The authors verify the specificity and sensitivity of this procedure by simulation and real data analysis in high-dimensional settings.

     

Rank-Based Test for Partial Functional Linear Regression Models

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models. Based on a rank score function, the authors develop a rank test using functional principal component analysis, and establish the asymptotic properties of the resulting test under null and local alternative hypotheses. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.     

A Tight Approximation Algorithm for Multi-Vehicle CVRP with Unsplittable Demands on a Line

In this paper, the authors study the multi-vehicle capacitated vehicle routing problem on a line-shaped network with unsplittable demand. The objective is to find a transportation scheme to minimize the longest distance traveled by a single vehicle such that all the customers are served without violating the capacity constraint. The authors show that this problem has no polynomial-time algorithm with performance ratio less than 2 on condition that P ≠ NP, and then provide a 2-approximation algorithm.

     

Inference for repeated measures models under heteroscedasticity

This paper considers a widely used mixed effects model in repeated measures under heteroscedasticity. Hypotheses of the equality of the fixed effects and the simultaneous confidence intervals for all pair-wise differences are discussed.A generalized F-test has been proposed to test the equality of the fixed effects in the model,but simulation results for evaluating its performance have not been shown in the literature.Moreover,the generalized F-test cannot be used to deduce the simultaneous confidence intervals for all pair-wise differences of the fixed effects.The authors propose two new p-values to test the hypotheses of equality of the fixed effects and simultaneous confidence intervals of the differences of the effects based on the generalized pivotal quantities derived in this paper.The authors also compare the empirical performances of the proposed tests and the generalized F-test. The typeⅠerror rates and powers of these tests are evaluated using the Monte Carlo simulation.The simulation studies show that the generalized F-test does not perform well in terms of typeⅠerror rate under various sample size and parameter combinations.However,the typeⅠerror probabilities of the proposed tests are always close to the nominal value.It can also be seen that the simultaneous confidence intervals perform well.     

A modified likelihood ratio test for homogeneity in bivariate normal mixtures of two samples

This paper investigates the asymptotic properties of a modified likelihood ratio statistic for testing homogeneity in bivariate normal mixture models of two samples. The asymptotic null distribution of the modified likelihood ratio statistic is found to be χ₂², where χ₂² is a chi-squared distribution with 2 degrees of freedom. This research was supported by the National Natural Science Foundation of China under Grant No. 10661003, SRF for ROCS, SEM under Grant No. [2004]527, and the Natural Science Foundation of Guangxi under Grant No. 0728092.     

Inference on coefficient function for varying-coefficient partially linear model

One important model in handling the multivariate data is the varying-coemcient partially linear regression model.In this paper,the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not.It is showed that the normalized proposed test follows asymptoticallyχ~2-distribution and the Wilks phenomenon under the null hypothesis,and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing.Some simulation studies illustrate that the test works well.     

Model checking for general linear error-in-covariables model with validation data

In this paper, model checking problem is considered for general linear model when covariables are measured with error and an independent validation data set is available. Without assuming any error model structure between the true variable and the surrogate variable, the author first apply nonparametric method to model the relationship between the true variable and the surrogate variable with the help of the validation sample. Then the author construct a score-type test statistic through model adjustment. The large sample behaviors of the score-type test statistic are investigated. It is shown that the test is consistent and can detect the alternative hypothesis close to the null hypothesis at the rate n ^−r with 0 ≤ r ≤ 1/2. Simulation results indicate that the proposed method works well.     

Regression Function Comparison for Paired Data

In this paper, the regression function comparison for paired data is studied. The proposed test statistic is based on the weighted integral of characteristic function marked by the difference of responses. There are several merits of the proposed statistic. For instance, it takes a simple V-statistic form. No bandwidth is needed. No moment conditions are required for covariates. It can be applied to covariates of any fixed dimension. The asymptotic results are also developed. It is proven that n times the proposed test statistic converges to a finite limit under the null hypothesis and the test is consistent against any fixed alternatives. Local alternative hypotheses which converge to the null hypothesis at the rate of n-1/2 are also detected. A suitable Bootstrap algorithm is also proposed for the implementation of the proposed test statistic. Simulation studies are carried out to illustrate the merits of the proposed method. A real data example is also used to illustrate the proposed testing procedures.     

Integral-type tests for goodness-of-fit

<正> A family of integral-type goodness-of-fit tests is investigated.This family includes someexisting tests,such as the Cramér-von Mises test and Anderson-Darling test,etc.The asymptoticdistributions of the tests in the family under the null and local alternative hypotheses are established.The almost sure convergence under a fixed underlying distribution is obtained.Furthermore,simulationsare conducted to compare the powers of the tests in the family.Simulation results show that fordifferent alternatives,the more powerful tests are different,and the parameter λ has great influence onthe tests in small sample cases.     

一类股市波动性预测模型的多变点检验

讨论了一类股市波动性预测模型的多变点检验问题.基于累积和(CUSUM)统计量,提出一种新的波动性预测模型多变点的拟似然比检验,在原假设下给出统计量的极限分布及渐近临界值的解析表达式.多变点的递归检验是本文的一个主要方面,即在检验时允许原假设含有变点,备择假设比原假设多一个变点,并在递归检验的过程中同时得到变点时刻与变点个数的一致估计,因此为处理含多变点的金融数据时确定变点个数提供了一种建模策略.数值模拟与实例分析说明了方法的有效性.     

Curvature-Based r-Adaptive Isogeometric Analysis with Injectivity-Preserving Multi-Sided Domain Parameterization

Inspired by the r-refinement method in isogeometric analysis, in this paper, the authors propose a curvature-based r-adaptive isogeometric method for planar multi-sided computational domains parameterized by toric surface patches. The authors construct three absolute curvature metrics of isogeometric solution surface to characterize its gradient information, which is more straightforward and effective. The proposed method takes the internal weights as optimization variables and the resulting par...