ESTIMATION AND INFERENCE FOR VARYING-COEFFICIENT REGRESSION MODELS WITH ERROR-PRONE COVARIATES

This paper studies the estimation and inference for a class of varying-coefficient regression models with error-prone covariates. The authors focus on the situation where the covariates are unobserved, there are no repeated measurements and the covariance matrix of the measurement errors is unknown, but some auxiliary information is available. The authors propose an instrumental variable type local polynomial estimator for the unknown varying-coefficient functions, and show that the estimator achieves the optimal nonparametric convergence rate, is asymptotically normal, and avoids using undersmoothing to allow the bandwidths to be selected using data-driven methods. A simulation is carried out to study the finite sample performance of the proposed estimator, and a real date set is analyzed to illustrate the usefulness of the developed methodology.     

CONTINUOUS AUXILIARY COVARIATE IN ADDITIVE HAZARDS REGRESSION FOR SURVIVAL DATA

This paper considers the additive hazards iliary covariate information to improve the efficiency regression analysis by utilizing continuous aux- of the statistical inference when the primary covariate is ascertained only for a randomly selected subsample. The authors construct a martingale based estimating equation for the regression parameter and establish the asymptotic consistency and normality of the resultant estimators. Simulation study shows that the proposed method can greatly improve the efficiency compared with the estimator which discards the auxiliary covariate information in a variety of settings. A real example is also provided as an illustration.     

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Aimed at the abominable influences to blind equaliza-tion algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization （DFE） algorithm adapting to different under-water acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in dif-ferent unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.     

Variable Selection of Varying Dispersion Student-t Regression Models

The Student-t regression model is a useful extension of the normal model,which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients.In this paper,the varying dispersion Student-t regression model is discussed,in which both the mean and the dispersion depend upon explanatory variables.The problem of interest is simultaneously select significant variables both in mean and dispersion model.A unified procedure which can simultaneously select significant variable is given.With appropriate selection of the tuning parameters,the consistency and the oracle property of the regularized estimators are established.Both the simulation study and two real data examples are used to illustrate the proposed methodologies.     

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.     

The adaptive LASSO spline estimation of single-index model

In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L ₁ penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO (aLASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models.     

Variable Selection for Structural Equation with Endogeneity

This paper studies variable selection problem in structural equation of a two-stage least squares (2SLS) model in presence of endogeneity which is commonly encountered in empirical economic studies. Model uncertainty and variable selection in the structural equation is an important issue as described in Andrews and Lu (2001) and Caner (2009). The authors propose an adaptive Lasso 2SLS estimator for linear structural equation with endogeneity and show that it enjoys the oracle properties, i.e., the consistency in both estimation and model selection. In Monte Carlo simulations, the authors demonstrate that the proposed estimator has smaller bias and MSE compared with the bridge-type GMM estimator (Caner, 2009). In a case study, the authors revisit the classic returns to education problem (Angrist and Krueger, 1991) using the China Population census data. The authors find that the education level not only has strong effects on income but also shows heterogeneity in different age cohorts.     

Variable selection in joint location,scale and skewness models of the skew-normal distribution

Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and (or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes, and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.     

WHO WON THE 2008 OLYMPICS？ A MULTICRITERIA DECISION OF MEASURING INTANGIBLES

It often happens that at the end of Olympics games the medals won by more than one country are many and close in total number as in the 2008 games where China won 100 medals with many gold ones and the United States won 110 medals but with a lesser number of gold medals. The question is： Although it is often done arbitrarily, is there a way to quantify the values of gold, silver and bronze medals legitimately to resolve this concern？ This short exposition shows that there is by using the author＇s theory for the measurement of intangibles, the Analytic Hierarchy Process.     

AUTOMATIC SELECTION OF PARAMETERS IN SPLINE REGRESSION VIA KULLBACK－LEIBLER INFORMATION

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Asymptotic and stable proper ties of general stochastic functional differential equations

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative up-per bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSal e-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the wel-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to il ustrate the usage of the method.     

Interactive multigraph visualization and exploration with a two-phase strategy

While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases： the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.     

Adaptive actuator failure compensation and disturbance rejection scheme for spacecraft

An adaptive actuator failure compensation scheme is proposed for attitude tracking control of spacecraft with unknown disturbances and uncertain actuator failures. A new feature of this adaptive control scheme is the adaptation of the failure pattern parameter estimates, as well as the failure signal parameter estimates, for direct adaptive actuator failure compensation. Based on an adaptive backstepping control design, the estimates of the disturbance parameters are used to solve the disturbance rejection problem. The unknown disturbances are compensated completely with the stability of the whole closed-loop system. The scheme is not only able to accommodate uncertain actuator failures, but also robust against unknown external disturbances. Simulation results verify the desired adaptive actuator failure compensation performance.     

FAMILIES OF p-ARY SEQUENCES WITH LOW CORRELATION AND LARGE LINEAR COMPLEXITY

This paper generalizes a method of generating shift sequences in the interleaved construc- tion proposed by Gong. With the new shift sequences, some new families of p-ary sequences with desired properties can be obtained. A lower bound on the number of new families of binary sequences is also established.     

Spectral analysis and stabilization of a coupled wave-ODE system

In this paper, an interconnected wave-ODE system with K-V damping in the wave equation and unknown parameters in the ODE is considered. It is found that the spectrum of the system operator is composed of two parts： Point spectrum and continuous spectrum. The continuous spectrum consists of an isolated point 1 1/d, and there are two branches of the asymptotic eigenvalues： The first branch is accumulating towards 1 -2, and the other branch tends to -∞. It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space. As a consequence, the spectrum-determined growth condition and exponential stability of the system are concluded.     

Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

Spiking neural P systems with anti-spikes （ASN P systems） are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules （identified by （a, a） and （a,a^-）） can achieve Turing completeness as number accepting devices.     

Optimal binary codes from one-lee weight codes and two-lee weight projective codes over ℤ4

This paper investigates the structures and properties of one-Lee weight codes and two-Lee weight projective codes over Z4.The authors first give the Pless identities on the Lee weight of linear codes over Z4.Then the authors study the necessary conditions for linear codes to have one-Lee weight and two-Lee projective weight respectively,the construction methods of one-Lee weight and two-Lee weight projective codes over Z4 are also given.Finally,the authors recall the weight-preserving Gray map from（Z4n,Lee weight）to（F2（2n）,Hamming weight）,and produce a family of binary optimal oneweight linear codes and a family of optimal binary two-weight projective linear codes,which reach the Plotkin bound and the Griesmer bound.     

Robust bounded control for uncertain flight dynamics using disturbance observer

The robust bounded flight control scheme is developed for the uncertain longitudinal flight dynamics of the fighter with control input saturation invoking the backstepping technique. To enhance the disturbance rejection ability of the robust flight control for fighters, the sliding mode disturbance observer is designed to estimate the compounded disturbance including the unknown external disturbance and the effect of the control input saturation. Based on the backstepping technique and the compounded disturbance estimated output, the robust bounded flight control scheme is proposed for the fighter with the unknown external disturbance and the control input saturation. The closed-loop system stability under the developed robust bounded flight control scheme is rigorously proved using the Lyapunov method and the uniformly asymptotical convergences of all closed-loop signals are guaranteed. Finally, simulation results are presented to show the effectiveness of the proposed robust bounded flight control scheme for the uncertain longitudinal flight dynamics of the fighter.     

Exact inference for two exponential populations with competing risks data

In a reliability comparative test, the joint censoring model is usually adopted to evaluate the performances of units with the same facility. However, most researchers ignore the pos- sibility that there is more than one factor for the failure when a test unit fails. To solve this problem, we consider a joint Type-II hybrid censoring model for the analysis of exponential competing failure data. Based on the maximum likelihood theory, we compute the maximum likelihood estimators （MLEs） of parameters and then obtain the condition ensuring MLEs existence for every unknown parameter. Then we derive the conditional exact distributions and corresponding moment properties for parameters by the moment generating function （MGF）. A Monte-Carlo simulation is conducted to compare the performances of different ways. And finally, we conduct a numerical example to illustrate the proposed method.     

Optimal redundancy allocation for reliability systems with imperfect switching

The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with sub- systems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution,