Comparisons among some predictors of exponential distributions using Pitman closeness

The bias, mean squared error and likelihood optimality criteria are used frequently to compare estimators and predictors. Recently, the probability of nearness around a statistic (estimator or predictor) has received a considerable attention in the literature. In this article, we adopt the Pitman’s measure of closeness (PMC) as an optimality criterion to compare the maximum likelihood, best linear unbiased, best linear invariant, median unbiased and conditional median predictors of a future ordered statistic based on a type II censored sample from an exponential distribution with unknown scale parameter. Numerical computations of the PMC for all comparisons among these predictors are performed and presented.     

On criteria for cn-consistency of estimators

Sulficient conditions are given for c_n-consistency of Bayesian and maximum likelihood estimators both in terms of variation distance and in so-called “predictable” terms.     

Pooling two censored samples from exponential populations

Preliminary test estimators are suggested for the scale parameter of an exponential population when observations become available from life test experiments. These estimators are shown to be efficient than the usual maximum likelihood estimator.     

Point prediction of future order statistics from an exponential distribution

Two-sample point prediction is considered for a two-parameter exponential distribution. Several point predictors such as the best unbiased predictor, best invariant predictor and maximum likelihood predictor are obtained for future order statistics on the basis of observed record values in two cases: where the location parameter is known and unknown. These predictors are compared in the sense of their mean squared prediction errors. Finally, some numerical results are given to illustrate the proposed procedures.     

Parameter Estimation of Nonlinear Stochastic Differential Equations: Simulated Maximum Likelihood versus Extended Kalman Filter and Itô-Taylor Expansion

This article compares several estimation methods for nonlinear stochastic differential equations with discrete time measurements. The likelihood function is computed by Monte Carlo simulations of the transition probability (simulated maximum likelihood SML) using kernel density estimators and functional integrals and by using the extended Kalman filter (EKF and second-order nonlinear filter SNF). The relation with a local linearization method is discussed. A simulation study for a diffusion process in a double well potential (Ginzburg–Landau equation) shows that, for large sampling intervals, the SML methods lead to better estimation results than the likelihood approach via EKF and SNF. A second study using a nonlinear diffusion coefficient (generalized Cox–Ingersoll–Ross model) demonstrates that the EKF type estimators may serve as efficient alternatives to simple maximum quasilikelihood approaches and Monte Carlo methods.     

A new procedure for the estimation of variance components

For the estimation of variance components in the one way random effects models, we propose some estimators which avoid negative and zero estimates of the variance component, a well-known problem with customary estimators such as the maximum likelihood or the restricted maximum likelihood estimators. The proposed estimators are shown to have lower mean squared error than customary estimators over a large range of the parameter space. This is also exhibited in a Monte Carlo study. Extensions of the proposed procedure to more complex situations are also discussed.     

A note on the missing value principle and the EM-Algorithm for estimation and prediction in sampling from finite populations with a multinormal superpopulation model

It is shown that both the missing value principle (MVP) of Orchard and Woodbury (1972) and the EM-Algorithm of Dempster, Laird and Rubin (1972) yield a unique predictor of the population total, under a superpopulation multinormal model. The predictor obtained is the maximum likelihood predictor introduced by Royal (1976).     

Maximum likelihood estimation of covariance matrices under simple tree ordering

The closed-form maximum likelihood estimators for the completely balanced multivariate one-way random effect model are obtained by Anderson et al. (Ann. Statist. 14 (1986) 405). It remains open whether there exist the closed-form maximum likelihood estimators for the more general completely balanced multivariate multi-way random effects models. In this paper, a new parameterization technique for covariance matrices is used to grasp the inside structure of likelihood function so that the maximum likelihood equations can be dramatically simplified. As such we obtain the closed-form maximum likelihood estimators of covariance matrices for Wishart density functions over the simple tree ordering set, which can then be applied to get the maximum likelihood estimators for the completely balanced multivariate multi-way random effects models without interactions.     

Pseudo-empirical likelihood estimation using nonparametric regression

Pseudo-empirical likelihood estimation of the population mean is considered. A nonparametric regression theory is proposed, to provide the fitted values on which to calibrate, and the common model misspecification problem is therefore addressed. Results derived from empirical studies show that the proposed estimator for the population mean can perform better than alternative estimators.     

Inference for ordered parameters in multinomial distributions

This paper discusses inference for ordered parameters of multinomial distributions. We first show that the asymptotic distributions of their maximum likelihood estimators (MLEs) are not always normal and the bootstrap distribution estimators of the MLEs can be inconsistent. Then a class of weighted sum estimators (WSEs) of the ordered parameters is proposed. Properties of the WSEs are studied, including their asymptotic normality. Based on those results, large sample inferences for smooth functions of the ordered parameters can be made. Especially, the confidence intervals of the maximum cell probabilities are constructed. Simulation results indicate that this interval estimation performs much better than the bootstrap approaches in the literature. Finally, the above results for ordered parameters of multinomial distributions are extended to more general distribution models. This work was supported by National Natural Science Foundation of China (Grant No. 10371126)     

Generalized variance estimators in the multivariate gamma models

It is already known that the uniformly minimum variance unbiased (UMVU) estimator of the generalized variance always exists for any natural exponential family. However, in practice, this estimator is often difficult to obtain. This paper provides explicit forms of the UMVU estimators for the bivariate and symmetric multivariate gamma models, which are diagonal quadratic exponential families. For the non-independent multivariate gamma models, it is shown that the UMVU and the maximum likelihood estimators are not proportional.      

Estimating parameters in one-way analysis of covariance model with short-tailed symmetric error distributions

We consider one-way analysis of covariance (ANCOVA) model with a single covariate when the distribution of error terms are short-tailed symmetric. The maximum likelihood (ML) estimators of the parameters are intractable. We, therefore, employ a simple method known as modified maximum likelihood (MML) to derive the estimators of the model parameters. The method is based on linearization of the intractable terms in likelihood equations. Incorporating these linearizations in the maximum likelihood, we get the modified likelihood equations. Then the MML estimators which are the solutions of these modified equations are obtained. Computer simulations were performed to investigate the efficiencies of the proposed estimators. The simulation results show that the proposed estimators are remarkably efficient compared with the conventional least squares (LS) estimators.     

The generalized linear exponential distribution

This paper deals with a new generalization of the linear exponential distribution. This distribution is called the generalized linear exponential distribution (GLED). Some statistical properties such as moments, modes and quantiles are derived. The failure rate function and the mean residual lifetime are also discussed. The maximum likelihood estimators of the parameters are obtained using a simulation study. Real data are used to determine whether the GLED is better than other well-known distributions in modeling lifetime data or not.     

Asymptotic properties of conditional maximum likelihood estimator in a certain exponential model

The conditional maximum likelihood estimator is suggested as an alternative to the maximum likelihood estimator and is favorable for an estimator of a dispersion parameter in the normal distribution, the inverse-Gaussian distribution, and so on. However, it is not clear whether the conditional maximum likelihood estimator is asymptotically efficient in general. Consider the case where it is asymptotically efficient and its asymptotic covariance depends only on an objective parameter in an exponential model. This remand implies that the exponential model possesses a certain parallel foliation. In this situation, this paper investigates asymptotic properties of the conditional maximum likelihood estimator and compares the conditional maximum likelihood estimator with the maximum likelihood estimator. We see that the bias of the former is more robust than that of the latter and that two estimators are very close, especially in the sense of bias-corrected version. The mean Pythagorean relation is also discussed.     

Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method

We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work out modified maximum likelihood equations by expressing the maximum likelihood equations in terms of ordered residuals and linearizing intractable nonlinear functions (Tiku and Suresh, 1992) [8]. The solutions, called modified maximum estimators, are explicit functions of sample observations and therefore easy to compute. They are under some very general regularity conditions asymptotically unbiased and efficient (Vaughan and Tiku, 2000) [4]. We show that for small sample sizes, they have negligible bias and are considerably more efficient than the traditional least squares estimators. We show that our estimators are robust to plausible deviations from an assumed distribution and are therefore enormously advantageous as compared to the least squares estimators. We give a real life example.     

定时截尾下指数分布的修正最大似然估计

本文在定时截尾情形下对指数分布提出了修正的最大似然估计，且把修正方法应用到定时截尾恒加试验和步加试验，模拟结果表明修正后的估计量的均方误差有了明显减少。     

Admissibility of the maximum likelihood estimator in the regression of two predictands on one predictor

Summary Stein [2] has shown that the maximum likelihood estimator (MLE) of the regression coefficients is admissible in unvariate regression with one predictor or with two predictors and known means. In a similar way it is shown in the present note that the MLE is admissible when there are two predictands and one predictor and the means are known.     

Reliability inference of stress–strength model for the truncated proportional hazard rate distribution under progressively Type-II censored samples

This paper considers the reliability inference for the truncated proportional hazard rate stress–strength model based on progressively Type-II censoring scheme. When the stress and strength variables follow the truncated proportional hazard rate distributions, the maximum likelihood estimation and the pivotal quantity estimation of stress–strength reliability are derived. Based on the percentile bootstrap sampling technique, the 95% confidence interval of stress–strength reliability is obtained, as well as the related coverage percentage. Moreover, based on the Fisher Z transformation and the modified generalized pivotal quantity, the 95% modified generalized confidence interval for the stress–strength reliability is obtained. The performance of the proposed method is evaluated by the Monte Carlo simulation. The numerical results show that the pivotal quantity estimators performs better than the maximum likelihood estimators. At last, two real datasets are analyzed by the proposed methodology for illustrative purpose. The results of real example analysis show that our model can be applied to the practical problem, the truncated proportional hazard rate distribution can fit the failure data better than other distributions, and the algorithms in this paper are suitable to handle the small sample data.     

Analysis,modeling, emergence & integration in complex systems: A modeling and integration framework & system biology

Analysis and Modeling is the first “phase” of understanding or developing a system. It is also, maybe more importantly, the foundation of understanding a natural science or system. It's abstract and conceptually difficult but, being foundational, contributes the most to the quality of understanding of (designed or natural) systems. Complex Systems have a natural hierarchy of levels and multiple subsystems. The character and functionality of each level or subsystem “emerges” across its boundaries. Both sides of these boundaries must be understood within that side's natural thought patterns. Integrated interdisciplinary collaboration is essential for making sense of complex systems; but collaboration among disciplines is difficult, because of their different ways of thinking. This creates a dilemma, “understanding complex systems” is one horn; “integrated interdisciplinary collaboration” is the other. This dilemma in complex system analysis/modeling and interdiscipline collaboration, is currently addressed by “grabbing the bull by the horns.” This takes on this doubly complex problem, by painstakingly building up abstract “bull wrestling” skills in and across domains and disciplines. There's another wrinkle; complexity requires interdisciplinary collaboration at deeper, more dissimilar, levels. The usual approach, finding a way to “pass between the horns of the dilemma” will not work here, due to this cross coupling. Rather than trying to pass between the horns, by abstracting away the coupling, we overtly organizing this coupling. We weave a semantic unification space of conceptual connections linking each side of a boundary to its appropriate way of thinking. This allows us to abstracting away the dilemma and iron out the wrinkle. The threads of common image schemas, cognitive metaphors and conceptual interfaces, weave a bridge between the semantics foundations and organizations of each problem. These allow addressing the problems synergistically. This paper presents and explores a naturally valid way for discipline specific and discipline integrating addressing complex systems. We start with the methodological insights from analysis and modeling from the perspective of object orientation, with its ontologies, organizing lexical semantics. We advance from there by integrating in imagistic, imaginative semantics and affordance based interaction methodology, as the keys to addressing complex systems analysis, modeling and integration. © 2007 Wiley Periodicals, Inc. Complexity, 2007     

Explicit estimators of parameters in the Growth Curve model with linearly structured covariance matrices

Estimation of parameters in the classical Growth Curve model, when the covariance matrix has some specific linear structure, is considered. In our examples maximum likelihood estimators cannot be obtained explicitly and must rely on optimization algorithms. Therefore explicit estimators are obtained as alternatives to the maximum likelihood estimators. From a discussion about residuals, a simple non-iterative estimation procedure is suggested which gives explicit and consistent estimators of both the mean and the linear structured covariance matrix.