Testing for the parametric parts in a single-index varying-coefficient model

Single-index varying-coefficient models (SIVCMs) are very useful in multivariate nonparametric regression.However,there has less attention focused on inferences of the SIVCMs.Using the local linear method,we propose estimates of the unknowns in the SIVCMs.In this article,our main purpose is to examine whether the generalized likelihood ratio (GLR) tests are applicable to the testing problem for the index parameter in the SIVCMs.Under the null hypothesis our proposed GLR statistic follows the chi-squared distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters or functions,which is called as Wilks’ phenomenon (see Fan et al.,2001).A simulation study is conducted to illustrate the proposed methodology.     

Stationary solution and parametric estimation for bilinear model driven by ARCH noises

Bilinear model driven by ARCH (1) noises is proposed. Existence, uniqueness and form of stationary solution to this new model are presented. Maximum likelihood estimation of the model is discussed and some simulation results are given to evaluate our algorithm.     

SEM modeling with singular moment matrices Part III: GLS estimation

We discuss generalized least squares (GLS) and maximum likelihood (ML) estimation for structural equations models (SEM), when the sample moment matrices are possibly singular. This occurs in several instances, for example, for panel data when there are more panel waves than independent replications or for time series data where the number of time points is large, but only one unit is observed. In previous articles, it was shown that ML estimation of the SEM is possible by using a correct Gaussian likelihood function. In this article, the usual GLS fit function is modified so that it is also defined for singular sample moment matrices S. In large samples, GLS and ML estimation perform similarly, and the modified GLS approach is a good alternative when S becomes nearly singular. Both GLS approaches do not work for N = 1, since here S = 0 and the modified GLS approach yields biased estimates. In conclusion, ML estimation (and pseudo ML under misspecification) is recommended for all sample sizes including N = 1.     

Clustering estimates for spatial point distributions with unstable potentials

Summary Several authors have tried to model highly clustered point patterns by using Gibbs distributions with attractive potentials. Some of these potentials violate a stability condition well known in statistical mechanics. We show that such potentials produce patterns which are much more tightly clustered than those considered by the authors. More generally, our estimates provide a useful test for rejecting unsuitable potentials in models for given patterns. We also use instability arguments to reject related approximations and simulations. Csiro     

A pendulum with an elliptic-type parametric excitation: Stability charts for a damped and undamped system

In this paper, a pendulum parametrically excited by the excitation which has the form of the Jacobi cn elliptic function is considered. Three cases related to the value of the elliptic parameter are distinguished: the case when it is smaller than zero, when it ranges between zero and unity, and when it is higher than unity. First, interpretations of the excitation with such elliptic parameter are given in terms of its period, higher harmonic content and the amplitude. These interpretations enable one to consider the elliptic-type excitation as a type of multi-cosine excitation whose frequency and amplitude are related mutually in a particular way. Stability charts are determined for damped and undamped systems. When the elliptic parameter is equal to zero, the governing equations considered transform to the well-known Mathieu equation. In all other cases, the governing equations considered can be seen as a new generalisation of the Mathieu equation. The influence of an arbitrary real elliptic parameter on the location and shape of the transition curves and instability tongues is investigated, illustrated and discussed in all three cases, which represent new and so far unknown results.     

Analysis of two-sample censored data using a semiparametric mixture model

In this article we study a semiparametric mixture model for the two-sample problem with right censored data. The model implies that the densities for the continuous outcomes are related by a parametric tilt but otherwise unspecified. It provides a useful alternative to the Cox （1972） proportional hazards model for the comparison of treatments based on right censored survival data. We propose an iterative algorithm for the semiparametric maximum likelihood estimates of the parametric and nonparametric components of the model. The performance of the proposed method is studied using simulation. We illustrate our method in an application to melanoma.     

Joint modeling of failure time data with transformation model and longitudinal data when covariates are measured with errors

Semiparametric transformation models provide a class of flexible models for regression analysis of failure time data. Several authors have discussed them under different situations when covariates are timeindependent (Chen et al., 2002; Cheng et al., 1995; Fine et al., 1998). In this paper, we consider fitting these models to right-censored data when covariates are time-dependent longitudinal variables and, furthermore, may suffer measurement errors. For estimation, we investigate the maximum likelihood approach, and an EM algorithm is developed. Simulation results show that the proposed method is appropriate for practical application, and an illustrative example is provided.     

A new smoothing-regularization approach for a maximum-likelihood estimation problem

We consider the problem min _i=1 ^m (aⁱ,x–b_iloga ⁱ, z) subject tox 0 which occurs as a maximum-likelihood estimation problem in several areas, and particularly in positron emission tomography. After noticing that this problem is equivalent to mind(b, Ax) subject tox 0, whered is the Kullback-Leibler information divergence andA, b are the matrix and vector with rows and entriesa ⁱ,b _i, respectively, we suggest a regularized problem mind(b, Ax) + d(v, Sx), where is the regularization parameter,S is a smoothing matrix, andv is a fixed vector. We present a computationally attractive algorithm for the regularized problem, establish its convergence, and show that the regularized solutions, as goes to 0, converge to the solution of the original problem which minimizes a convex function related tod(v, Sx). We give convergence-rate results both for the regularized solutions and for their functional values.The research of A. N. Iusem was partially supported by CNPq Grant No. 301280/86-MA.     

Empirical Gramian-based spatial basis functions for model reduction of nonlinear distributed parameter systems

Correct selection of spatial basis functions is crucial for model reduction for nonlinear distributed parameter systems in engineering applications. To construct appropriate reduced models, modelling accuracy and computational costs must be balanced. In this paper, empirical Gramian-based spatial basis functions were proposed for model reduction of nonlinear distributed parameter systems. Empirical Gramians can be computed by generalizing linear Gramians onto nonlinear systems, which results in calculations that only require standard matrix operations. Associated model reduction is described under the framework of Galerkin projection. In this study, two numerical examples were used to evaluate the efficacy of the proposed approach. Lower-order reduced models were achieved with the required modelling accuracy compared to linear Gramian-based combined spatial basis function- and spectral eigenfunction-based methods.     

A perturbation analysis of a mechanical model for stable spatial patterning in embryology

Summary We investigate a mechanical cell-traction mechanism that generates stationary spatial patterns. A linear analysis highlights the model's potential for these heterogeneous solutions. We use multiple-scale perturbation techniques to study the evolution of these solutions and compare our solutions with numerical simulations of the model system. We discuss some potential biological applications among which are the formation of ridge patterns, dermatoglyphs, and wound healing.     

A transformed random effects model with applications

This paper proposes a transformed random effects model for analyzing non-normal panel data where both the response and (some of) the covariates are subject to transformations for inducing flexible functional form, normality, homoscedasticity, and simple model structure. We develop a maximum likelihood procedure for model estimation and inference, along with a computational device which makes the estimation procedure feasible in cases of large panels. We provide model specification tests that take into account the fact that parameter values for error components cannot be negative. We illustrate the model and methods with two applications: state production and wage distribution. The empirical results strongly favor the new model to the standard ones where either linear or log-linear functional form is employed. Monte Carlo simulation shows that maximum likelihood inference is quite robust against mild departure from normality. Copyright © 2009 John Wiley & Sons, Ltd.     

A modified adaptive Lasso for identifying interactions in the Cox model with the heredity constraint

In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment–subgroup interactions in clinical trials, and gene–gene or gene–environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction’s component variables are included as main effects. We propose a modified Lasso method for the Cox regression model that adaptively selects important single covariates and pairwise interactions while enforcing the strong heredity constraint. The proposed method is based on a modified log partial likelihood including two adaptively weighted penalties, one for main effects and one for interactions. A two-dimensional tuning parameter for the penalties is determined by generalized cross-validation. Asymptotic properties are established, including consistency and rate of convergence, and it is shown that the proposed selection procedure has oracle properties, given proper choice of regularization parameters. Simulations illustrate that the proposed method performs reliably across a range of different scenarios.     

A markov process model for software reliability analysis

Software reliability is a rapidly developing discipline. In this paper we model the fault-detecting processes by Markov processes with decreasing jump intensity. The intensity function is suggested to be a power function of the number of the remaining faults in the software. The models generalize the software reliability model suggested by Jelinski and Moranda (‘Software reliability research’, in W. Freiberger (ed.), Statistical Computer Performance Evaluation, Academic Press, New York, 1972. pp. 465–497). The main advantage of our models is that we do not use the assumption that all software faults correspond to the same failure rate. Preliminary studies suggest that a second-order power function is quite a good approximation. Statistical tests also indicate that this may be the case. Numerical results show that the estimation of the expected time to next failure is both reasonable and decreases relatively stably when the number of removed faults is increased.     

A parametric linear complementarity technique for the computation of equilibrium prices in a single commodity spatial model

This paper presents a parametric linear complementarity technique for the computation of equilibrium prices in a single commodity spatial model. We first reformulate the model as a linear complementarity problem and then apply the parametric principal pivoting algorithm for its solution. This reformulation leads to the study of an arc—arc weighted adjacency matrix associated with a simple digraph having weights on the nodes. Several basic properties of such a matrix are derived. Using these properties, we show how the parametric principal pivoting algorithm can be greatly simplified in this application. Finally, we report some computational experience with the proposed technique for solving some large problems.     

A spatial modal logic with a location interpretation

A spatial modal logic (SML) is introduced as an extension of the modal logic S4 with the addition of certain spatial operators. A sound and complete Kripke semantics with a natural space (or location) interpretation is obtained for SML. The finite model property with respect to the semantics for SML and the cut‐elimination theorem for a modified subsystem of SML are also presented. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

一类具有空间异质和扩散的SVIR模型的全局稳定性

考虑了一类具有空间异质和反应扩散的SVIR传染病模型.当基本再生数等于1时,假定扩散系数为常数,证明了系统的无病平衡态是全局渐近稳定的.     

A Center Cutting Plane Algorithm for a Likelihood Estimate Problem

We describe a method for solving the maximum likelihood estimate problem of a mixing distribution, based on an interior cutting plane algorithm with cuts through analytic centers. From increasingly refined discretized statistical problem models we construct a sequence of inner non-linear problems and solve them approximately applying a primal-dual algorithm to the dual formulation. Refining the statistical problem is equivalent to adding cuts to the inner problems.     

基于删失数据的指数威布尔分布最大似然估计的新算法

本文讨论了指数威布尔分布当观测数据是删失数据情形时参数的最大似然估计问题.因为删失数据是一种不完全数据,我们利用EM算法来计算参数的近似最大似然估计.由于EM算法计算的复杂性,计算效率也不理想.为了克服牛顿-拉普森算法和EM算法的局限性,我们提出了一种新的方法.这种方法联合了指数威布尔分布到指数分布的变换和等效寿命数据的技巧,比牛顿-拉普森算法和EM算法更具有操作性.数据模拟讨论了这一方法的可行性.为了演示本文的方法,我们还提供了一个真实寿命数据分析的例子.     

A fractional‐order model for chronic lymphocytic leukemia and immune system interactions

In this study, a mathematical, fractional‐order model was developed for B cell chronic lymphocytic leukemia, with immune system, and then analyzed. Interactions between B leukemia cells, natural killer cells, cytotoxic T cells, and T‐helper cells are considered to be incorporated into a system consisting of four fractional differential equations. For estimation of the parameters, clinical data of six patients were used. By numerical solution of the system, the interactions between the leukemia cell population and the immune system cell populations for values of α ∈ (0,1) at different times were explained. By determining points of equilibrium and stability of the system were met. Bifurcation analysis showed that use of the fractional‐order model, figure out unpredictable behaviors of the system such as saddle‐node, bistability and hysteresis phenomenon occurred in the system by changing the values of some of the parameters, it was predictable.     

CONSISTENCY OF MLE OF THE PARAMETER OF EXPONENTIAL LIFETIME DISTRIBUTION FOR RANDOM CENSORING MODEL WITH INCOMPLETE INFORMATION

In this paper, we have discussed a random censoring test with incomplete information, and proved that the maximum likelihood estimator (MLE) of the parameter based on the randomly censored data with incomplete information in the case of the exponential distribution has the strong consistency.