Approximations of steady periodic water waves in flows with constant vorticity

We provide high-order approximations to periodic travelling wave profiles and to the velocity field and the pressure beneath the waves, in flows with constant vorticity over a flat bed.     

On the symmetry of equatorial travelling water waves with constant vorticity and stagnation points

The aim of this paper is to prove that equatorial travelling water waves at the surface of water flows with constant vorticity are symmetric, provided they have a profile that is monotonic between crests and troughs and that there are no stagnation points in the subsurface region.     

Estimation of relative risk using a log-binomial model with constraints

The relative risk/prevalence ratio and odds ratio are very popular in medical research and epidemiological studies. The odds ratio is overused in practice due to its direct relation with the logistic regression. Interpreting the odds ratio in terms of “relative risk” may lead to incorrect inference on the prevalence of certain event. In this paper, we propose to estimate the relative risk using the log-binomial model by maximizing the likelihood with a linear constraint, which can be easily implemented by an existing function, such as “constrOptim” in R. Furthermore, we review other existing methods and compare their performance under a variety of settings using simulated data. We suggest the proposed and COPY methods in practice, based on its performance in the simulations. For illustration, we investigate the vigorous physical activity effects on obesity using data from the National Health and Nutrition Examination Survey, and the treatment effects on lung cancer mortality using data in R.     

Maximum likelihood estimation from fuzzy data using the EM algorithm

A method is proposed for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observed-data likelihood defined as the probability of the fuzzy data. It is shown that the EM algorithm may be used for that purpose, which makes it possible to solve a wide range of statistical problems involving fuzzy data. This approach, called the fuzzy EM (FEM) method, is illustrated using three classical problems: normal mean and variance estimation from a fuzzy sample, multiple linear regression with crisp inputs and fuzzy outputs, and univariate finite normal mixture estimation from fuzzy data.     

有缺失数据的2×2×2列联表的参数估计

本文讨论了缺失数据的 2× 2× 2列联表的参数估计问题 ,通过给出适当的模型和观测数据模式 ,我们给出了各列联表各细胞参数的估计 ,并且证明这些估计是G -Markov的     

有噪声场合自回归模型的参数估计及定阶

本文讨论了有噪声场合自回归模型的参数估计及定阶问题 ,还给出了噪声模型的参数估计 ,证明了它们的强相容性 ,并进行了数值模拟计算     

Estimation of partially specified spatial panel data models with random-effects

In this article, we study estimation of a partially specified spatial panel data linear regression with random-effects. Under the conditions of exogenous spatial weighting matrix and exogenous regressors, we give an instrumental variable estimation. Under certain sufficient assumptions, we show that the proposed estimator for the finite dimensional parameter is root-N consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and asymptotically distributed. Consistent estimators for the asymptotic variance-covariance matrices of both the parametric and unknown components are provided. The Monte Carlo simulation results verify our theory and suggest that the approach has some practical value.     

Estimation of partial linear error-in-response models with validation data

In this paper, an estimation theory in partial linear model is developed when there is measurement error in the response and when validation data are available. A semiparametric method with the primary data is used to define two estimators for both the regression parameter and the nonparametric part using the least squares criterion with the help of validation data. The proposed estimators of the parameter are proved to be strongly consistent and asymptotically normaal, and the estimators of the nonparametric part are also proved to be strongly consistent and weakly consistent with an optimal convergent rate. Then, the two estimators of the parameter are compared based on their empirical performances. Supported by NNSF of China (No. 10231030, No. 10241001) and a grant to the author for his excellent Ph.D. dissertation work in China.     

Estimation of reliability using failure-degradation data with explanatory variables

We consider semiparametric estimation of characteristics of degradation and failure process using degradation and multi-mode failure time data with covariates under the assumption that the component of hazard rate related to observable degradation is an unknown function of degradation and may depend on covariates. Bibliography: 17 titles.     

无失效数据情形可靠性参数的估计和调整

本文在无失效取样情形下,提出了产品可靠性参数的一种估计和调整的方法———加权多层Bayes估计法.在无失效数据情形下失效率的多层Bayes估计和引进失效信息后失效率的多层Bayes估计的基础上,对可靠性参数进行了估计和调整———给出了失效率和可靠度的加权多层Bayes估计.最后,结合发动机的实际问题进行了计算,结果表明本文提出的方法可行且便于应用.     

基于无失效数据的可靠度的估计

对二项分布无失效数据，在可靠度的先验密度为且时，给出了可靠度的多层Bayes估计．     

Estimation of parameter matrices based on measured data

Finite element structural updating based on measured data may inherent significant errors due to uncertainties in the updated physical parameter matrices. This study presents analytical equations to estimate the change in the physical parameter matrices based on the measured modal data of dynamic systems and the measured displacement data of static systems. The equations for the parameter estimation are derived by minimizing cost functions in the satisfaction of the eigenvalue equation, the mode shape orthogonality requirements for the dynamic system, and the satisfaction of the measured displacement data for the static systems. The proposed method utilizes the Moore–Penrose inverse for the inverse of the rectangular matrices without using Lagrange multipliers. Comparing the analytical results with Berman & Nagy’s method and Yang & Chen’s method, this study demonstrates that the derived equations take simpler forms and produce more accurate results. The proposed method can be widely utilized in predicting static or dynamic parameter matrices for the design and analysis of any structure.     

Estimation of a change-point in the mean function of functional data

The paper develops a comprehensive asymptotic theory for the estimation of a change-point in the mean function of functional observations. We consider both the case of a constant change size, and the case of a change whose size approaches zero, as the sample size tends to infinity. We show how the limit distribution of a suitably defined change-point estimator depends on the size and location of the change. The theoretical insights are confirmed by a simulation study which illustrates the behavior of the estimator in finite samples.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Parametric Estimation of a Boolean Segment Process with Stochastic Restoration Estimation

Abstract

We propose a stochastic restoration estimation (SRE) algorithm to estimate the parameters of the length distribution of a boolean segment process. A boolean segment process is a stochastic process obtained by considering the union of independent random segments attached to random points independently scattered on the plane. Each iteration of the SRE algorithm has two steps: first, censored segments are restored; second, based on these restored data, parameter estimations are updated. With a usually straightforward implementation, this algorithm is particularly interesting when censoring effects are difficult to take into account. We illustrate this method in two situations where the parameter of interest is either the mean of the segment length distribution or the variance of its logarithm. Its application to vine shoot length distribution estimation is presented.     

利用SAS PROC MIXED分析混合线性模型非平衡试验数据

统计软件SAS中PROCMIXED的发展使得混合线性模型非平衡试验数据的分析在技术上变得简单可行。但在小样本非平衡数据条件下,选择怎样的自由度计算方法用于固定效应统计检验,才能取得较准确的分析结果,则是一个尚未明确的问题。本文利用PROCMIXED提供的几个自由度计算方法对3个常用农业试验设计的非平衡数据进行了分析。MonteCarlo模拟研究结果表明,Kenward Roger法能够取得第一类统计错误率的最佳控制。     

局部资料下变分同化方法的稳定性研究

对于简化的一维扩散方程,在局部观测资料下,研究变分同化方法的稳定性.在变分同化中结合正则化方法,选择合适的正则化参数和稳定泛函,对预报模式进行修正,通过对预报精度进行先验估计,证明了该方法对于一维扩散方程的解的稳定性.修正补充相关计算结果,最后举出一个反例说明稳定性泛函的选取对于改进的变分同化方法实施的重要性.     

Experimental study of scheduling with memory constraints using hybrid methods

In this paper we study divisible load scheduling in systems with limited memory. Divisible loads are parallel computations which can be divided into independent parts processed in parallel on remote computers, and the part sizes may be arbitrary. The distributed system is a heterogeneous single level tree. The total size of processor memories is too small to accommodate the whole load at any moment of time. Therefore, the load is distributed in many rounds. Memory reservations have block nature. The problem consists in distributing the load taking into account communication time, computation time, and limited memory buffers so that the whole processing finishes as early as possible. This problem is both combinatorial and algebraic in nature. Therefore, hybrid algorithms are given to solve it. Two algorithms are proposed to solve the combinatorial component. A branch-and-bound algorithm is nearly unusable due to its complexity. Then, a genetic algorithm is proposed with more tractable execution times. For a given solution of the combinatorial part we formulate the solution of the algebraic part as a linear programming problem. An extensive computational study is performed to analyze the impact of various system parameters on the quality of the solutions. From this we were able to infer on the nature of the scheduling problem.     

Estimation of semi-varying coefficient model with surrogate data and validation sampling

In this paper, we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error. With the help of validation sampling, we propose two estimators of the parameter and the coefficient functions by combining dimension reduction and the profile likelihood methods without any error structure equation specification or error distribution assumption. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the proposed estimators achieves the best convergence rate. Data-driven bandwidth selection methods are also discussed. Simulations are conducted to evaluate the finite sample property of the estimation methods proposed.