Distance-Based Regression Analysis for Measuring Associations

Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies, genomic analyses, and many other research areas. Based on it, a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-F statistic has not yet been well established in the literature. To fill this gap, the authors study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large, the authors propose a square-root F-type test statistic which replaces the similarity matrix with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-F test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway.

     

RELATIVE FAILURE RATE AND ITS TEST

In this paper we develop scale and location invariant tests using U statistic forconstant relative failure rate against monotone relative failure rate.Monotone power andasymptotic normality of the test statistic are obtained.Some examples are presented,including tests for exponentiality.Weibull distribution and the two-sample problem.     

A NEW TEST PROCEDURE FOR NEW BETTER THAN USED IN INCREASING CONVEX ORDERING

A new nonparametric procedure is developed to test the exponentiality against the strict NBUC property of a life distribution. The exact null distribution is derived by the theory of sample spacings, and the asymptotic normality is also established by the large sample theory of L-statistics. Finally, the lower and upper tailed probability of the exact null distribution and some numerical simulation results are presented as well.     

Network's Relationship Flow-Behavior Theorems — Structural Theory of General Systems Applied in Networks

On the basis of several new concepts, such as, the relationship flow and the basic level of a network, and by applying the Structural Theory of General Systems, this article conducts a study of the relationships and their laws between the relationship flows, including information flows, matter flows, energy flows, fund flows, personnel flows, and so on, and the behavior or function of a network, and mathematically obtains two theorems, namely the network's relationship flow-behavior theorem 1 and 2. It then proposes a new model of networks, called the relationship flow-behavior model of networks, which shows that for a network, its behavior or function is determined and governed only by its input flow Rf(t) and relationship flow set Rf_nb(t) on its so-called basic level H_b, that is, its total relationship flows TRf(t)= Rf(t) ∪ Rf_nb(t), especially by the relationship flow cycle(s) in TRf(t). By a further mathematical study of the model, it can be expected to obtain a series of inherent relationships and their laws between the relationship flows and the behavior or function of a network, as well as their derivates, for example, the issues, ideas, theories, methods, approaches, and tools of network research, which would make significant contributions to the advancement of network research.     

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.     

Generalized F-test for high dimensional regression coefficients of partially linear models

This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown nonlinear component by some nonparametric methods and then generalize the F-statistic to test the regression coefficients under some regular conditions. During this procedure, the estimation of the nonlinear component brings much challenge to explore the properties of generalized F-test. The authors obtain some asymptotic properties of the generalized F-test in more general cases,including the asymptotic normality and the power of this test with p/n ∈(0, 1) without normality assumption. The asymptotic result is general and by adding some constraint conditions we can obtain the similar conclusions in high dimensional linear models. Through simulation studies, the authors demonstrate good finite-sample performance of the proposed test in comparison with the theoretical results. The practical utility of our method is illustrated by a real data example.     

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.     

Heart rate variability during high-intensity exercise

The aim of this paper is to describe and analyse the behaviour of heart rate variability (HRV) during constant-load, high-intensity exercise using a time frequency analysis (Wavelet Transform). Eleven elite cyclists took part in the study (age: 18.6±3.0 years; VO_2max: 4.88±0.61 litres·min^?1). Initially, all subjects performed an incremental cycloergometer test to determine load power in a constant load-test (379.55±36.02 W; 89.0%). HRV declined dramatically from the start of testing (p <0.05). The behaviour of power spectral density within the LF band mirrored that of total energy, recording a significant decrease from the outset LF peaks fell rapidly thereafter, remaining stable until the end of the test. HF-VHF fell sharply in the first 20 to 30 seconds. The relative weighting (%) of HF-VHF was inverted with the onset of fatigue, [1.6% at the start, 7.1 (p <0.05) at the end of the first phase, and 43.1% (p <0.05) at the end of the test]. HF-VHF_peak displayed three phases: a moderate initial increase, followed by a slight fall, thereafter increasing to the end of the test. The LF/HF-VHF ratio increased at the start, later falling progressively until the end of the first phase and remaining around minimal values until the end of the test.     

H 2/H ∞ control problems of backward stochastic systems

This paper is concerned with the mixed H2/H∞ control problem for a new class of stochastic systems with exogenous disturbance signal.The most distinguishing feature,compared with the existing literatures,is that the systems are described by linear backward stochastic differential equations（BSDEs）.The solution to this problem is obtained completely and explicitly by using an approach which is based primarily on the completion-of-squares technique.Two equivalent expressions for the H2/H∞ control are presented.Contrary to forward deterministic and stochastic cases,the solution to the backward stochastic H2/H∞ control is no longer feedback of the current state;rather,it is feedback of the entire history of the state.     

Optimal Design for Accelerated Degradation Tests with Several Stresses based on Wiener Process

Design for accelerated degradation tests (ADTs) in most literature concentrates on the case of just one stress, but the performance of many highly reliable products are affected by several stresses simultaneously, so it is necessary to study the optimal models for the ADTs with several stresses. This article first introduces a basic modeling framework of degradation path and then proposes an optimal model for ADTs with several stresses based on Wiener process. Under the constraint of total experimental cost, sample size, measurement times, and measurement frequency are obtained by minimizing the asymptotic variance of the estimated 100_p^th percentile of the product's lifetime distribution. Finally, a numerical example is presented to illustrate the proposed model.     

Analysis of batch arrival queue with randomized vacation policy and an un-reliable server

This paper examines an M^[x]G/1 queueing system with an unreliable server and a delayed repair,in which the server operates a randomized vacation policy with multiple vacations.Whenever the system is empty,the server immediately takes a vacation.If there is at least one customer found waiting in the queue upon returning from a vacation,the server will be immediately activated for service.Otherwise,if no customers are waiting for service at the end of a vacation,the server either remains idle with probability p or leaves for another vacation with probability 1-p.Whenever one or more customers arrive when the server is idle,the server immediately starts providing service for the arrivals.The server may also meet an unpredictable breakdown and the repair may be delayed.For such a system the authors derive the distributions of some important system characteristics,such as the system size distribution at a random epoch and at a departure epoch,the system size distribution at the busy period initiation epoch,and the distribution of the idle period and the busy period.The authors perform a numerical analysis for changes in the system characteristics,along with changes in specific values of the system parameters.A cost effectiveness maximization model is constructed to explain the benefits of such a queueing system.     

最大熵分布算法及其应用

分布估计算法是一种新的基于种群进化的算法,它通过统计当前群体中较优个体的信息构建其概率分布模型,然后对模型进行抽样生成下一代群体。针对分布估计算法在求解搜寻最优网络结构的NP-Hard问题,提出一种新的最大熵分布算法,该算法以Jaynes原理为依据,利用随机变量的最大熵估计随机变量的最小偏见概率分布,并以此作为算法的进化模型,有效地降低了算法的计算复杂度。以旅行商问题和误导问题为例所进行的计算结果证明了该算法具有更高的全局搜索能力与更稳定的收敛性。     

Some inverse optimization problems on network

In this paper, the authors consider some inverse problems on network, such as the inverse transport problems with gains (IGTP) and the inverse linear fractional minimum cost flow problem (IFFP). Firstly, the authors give the mathematics model of (IGTP) and an efficient method of solving it under l ₁ norm; Secondly, taking advantage of the optimality conditions, the authors consider the (IFFP) and give a simple method of solving it. Finally, an numerical example test is also developed.     

GENERALIZED p-VALUES AND GENERALIZED CONFIDENCE INTERVALS FOR VARIANCE COMPONENTS IN GENERAL RANDOM EFFECT MODEL WITH BALANCED DATA

Various random models with balanced data that are relevant for analyzing practical test data are described, along with several hypothesis testing and interval estimation problems concerning variance components. In this paper, we mainly consider these problems in general random effect model with balanced data. Exact tests and confidence intervals for a single variance component corresponding to random effect are developed by using generalized p-values and generalized confidence intervals. The resulting procedures are easy to compute and are applicable to small samples. Exact tests and confidence intervals are also established for comparing the random-effects variance components and the sum of random-effects variance components in two independent general random effect models with balanced data. Furthermore, we investigate the statistical properties of the resulting tests. Finally, some simulation results on the type Ⅰ error probability and power of the proposed test are reported. The simulation results indicate that exact test is extremely satisfactory for controlling type Ⅰ error probability.     

Articulated Estimator Random Field and Geometrical Approach Applied in System Identification

A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field (AERF) is a manifold or stratum of a stratified space with Riemannian geometry properties. From a high level excursion set, a probability distribution model M^sta is presented and a system model validation geometric criterion (SYMOVAGEC) for system model structures M^sys based on Riccian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.     

Empirical likelihood confidence region for parameters in linear errors-in-variables models with missing data

The multivariate linear errors-in-variables model when the regressors are missing at random in the sense of Rubin (1976) is considered in this paper. A constrained empirical likelihood confidence region for a parameter β ₀ in this model is proposed, which is constructed by combining the score function corresponding to the weighted squared orthogonal distance based on inverse probability with a constrained region of β ₀. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard chi-square distribution. Simulations show that the coverage rate of the proposed confidence region is closer to the nominal level and the length of confidence interval is narrower than those of the normal approximation of inverse probability weighted adjusted least square estimator in most cases. A real example is studied and the result supports the theory and simulation’s conclusion.     

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.     

CLT of wavelet estimator in semiparametric model with correlated errors

This paper considers the semiparametric regression model j/j= Xif3+g（U）+Vi（1 < i < n）, where（x_i,t_i） are known design points,/3 is an unknown slope parameter,g（·） is an unknown function, the correlated errors Vi=∑_j=-∞^∞c_je_i-jwith∑_j=-∞^∞|c_j|<∞,and e_i negatively associated random variables.Under appropriate conditions,the authors study the asymptotic normality for wavelet estimators ofβand g（·）.A simulation study is undertaken to investigate finite sample behavior of the estimators.     

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.     

How Local Participation in National Planning Creates New Development Opportunities

This study looks into the role of participation in influencing the adaptation of national policies to generate local economic development opportunities. The main question put forward is: What are the preconditions for creating new local development opportunities in a national state’s government planning process? The subject of the study is a planning process for the designation of a national park in the southwestern part of Norway. The analysis looks into three factors from the perspective of power: the mobilization and power base development of local stakeholders, the planning authority’s intention and structure for participation and change, and how conflicts are handled in the dialogue arenas. The conclusion with respect to regional innovation literature is that local stakeholders could make a difference by articulating both power over and power with and that conflicts may be useful if the planning authority takes the participants’ claims seriously by showing compromise or allowing integration of interests. From such preconditions new local economic opportunities could be created by collaboration.