Nonlinear and non-gaussian state estimation: A quasi-optimal estimator

The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hiirzeler and Kiinsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Poison and StofFer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on noniinearity and nonnormality of the system in the sense of the root mean square error criterion, which reason comes from slow convergence of the Gibbs sampler. Taking into account these problems, we propose the nonlinear and non-Gaussian filter and smoother which have much less computational burden and give us relatively better state estimates, although the proposed estimator does not yield the optimal state estimates in the sense of the minimum mean square error. The proposed filter and smoother are called the quasi-optimal filter and quasi-optimal smoother in this paper. Finally, through some Monte Carlo studies, the quasi-optimal filter and smoother are compared with the rejection sampling procedure and the Markov chain Monte Carlo procedure.     

A semigroup approach to the strong ergodic theorem of the multistate stable population process

In this paper we first formulate the dynamics of multistate stable population processes as a partial differential equation. Next, we rewrite this equation as an abstract differential equation in a Banach space, and solve it by using the theory of strongly continuous semigroups of bounded linear operators. Subsequently, we investigate the asymptotic behavior of this semigroup to show the strong ergodic theorem which states that there exists a stable distribution independent of the initial distribution. Finally, we introduce the dual problem in order to obtain a logical definition for the reproductive value and we discuss its applications.     

On a test for structural stability of euler conditions parameters estimated via the generalized method of moments estimator: small sample properties

This paper presents Monte Carlo experiments on the small sample performance of the predictive test for structural change proposed by Ghysels and Hall. The predictive test was found to be more powerful than the overidentifying restrictions test in terms of size-corrected power when a shift in parameter has occurred. Also, it was found that the power of the predictive test decreases drastically as the number of the out-sample data decreases.     

Optimization problems for consecutive-2-out-of-n:G system

Abstract

This paper considers the optimization problems for a consecutive-2-out-of-n:G system where n is considered to be fixed or random. When the number of components is constant, the optimal number of components and the optimal replacement time are discussed by minimizing the expected cost rates. Furthermore, we focus on the above discussions again when n is a random variable. We give an approximate value of MTTF and propose the preventive replacement policy, respectively.     

A review of RFID technology and its managerial applications in different industries

Radio frequency identification (RFID) is an emerging technology that is increasingly being used in supply chain management. RFID technology plays an important role in supporting logistics and supply chain processes because of their ability to identify, trace and track information throughout the supply chain. The technology can provide suppliers, manufacturers, distributors and retailers precise real time information about the products. This accurate knowledge of the inventory would result in lower labor cost, simplified business processes and improved supply chain efficiency. If properly used, it has the potential to cut ordering lead time and cost on inventory control, increase the accuracy of inventory information, help avoid stockouts and boost the number of inventory turns. In this paper, we provide an overview of the current state of RFID applications in different industries and its impact on business operations. We provide extensive literature survey and develop a framework for future research areas in this field.     

Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments

SUMMARY Non-parametric tests that deal with two samples include scores tests (such as the Wilcoxon rank sum test, normal scores test, logistic scores test, Cauchy scores test, etc.) and Fisher's randomization test. Because the non-parametric tests generally require a large amount of computational work, there are few studies on small-sample properties, although asymptotic properties with regard to various aspects were studied in the past. In this paper, the non-parametric tests are compared with the t -test through Monte Carlo experiments. Also, we consider testing structural changes as an application in economics.     

Effects of spatio-temporal intervals between newly-hatched larvae on larval survival and development inMonochamus alternatus (Coleoptera: Cerambycidae)

The effects of distance between hatching larvae on survival and development were investigated inMonochamus alternatus. Two newly-hatched larvae were inoculated intoPinus densiflora bolts at a distance of 2.5 cm or 10 cm, simultaneously or at an interval of 2 weeks. Some larvae were inoculated singly as a control. When larvae were inoculated simultaneously, mortality of the closely-inoculated larvae was significantly higher than that of distantly-inoculated larvae. Such high mortality was identified as due to conspecific bites. When the two larvae were inoculated asynchronously, the first-inoculated larvae killed some second-inoculated larvae but were never killed by them. Consequently, mortality was higher in second-inoculated larvae than in first-inoculated larvae. In particular, there was a significant difference in mortality between them when the larvae had been inoculated closely. The mortality of second-inoculated larvae was higher in the closely-inoculated group than in the distantly-inoculated group although there was no significant difference between them. In the case of two simultaneously-inoculated larvae, the initial distance between them had no significant effect on the development and growth in the early larval stage. When the larvae were inoculated asynchronously, the first-inoculated larvae grew more quickly than singly-inoculated control larvae.     

On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods

In the case where the lagged dependent variables are included in the regression model, it is known that the ordinary least squares estimates (OLSE) are biased in small sample and that the bias increases as the number of the irrelevant variables increases. In this paper, based on the bootstrap methods, an attempt is made to obtain the unbiased estimates in autoregressive and non-Gaussian cases. We propose the residual-based bootstrap method in this paper. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not. We obtain the results that it is possible to recover the true parameter values and that the proposed procedure gives us the less biased estimators than OLSE. This paper is a substantial revision of Tanizaki (2000). The normality assumption is adopted in Tanizaki (2000), but it is not required in this paper. The authors are grateful to an anonymous referee for valuable suggestions and comments. This research was partially supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (C)(2) #14530033, 2002–2005, for H. Tanizaki and Grants-in-Aid for the 21st Century COE Program.     

Sample size calculations for single-arm survival studies using transformations of the Kaplan–Meier estimator

In single-arm clinical trials with survival outcomes, the Kaplan–Meier estimator and its confidence interval are widely used to assess survival probability and median survival time. Since the asymptotic normality of the Kaplan–Meier estimator is a common result, the sample size calculation methods have not been studied in depth. An existing sample size calculation method is founded on the asymptotic normality of the Kaplan–Meier estimator using the log transformation. However, the small sample properties of the log transformed estimator are quite poor in small sample sizes (which are typical situations in single-arm trials), and the existing method uses an inappropriate standard normal approximation to calculate sample sizes. These issues can seriously influence the accuracy of results. In this paper, we propose alternative methods to determine sample sizes based on a valid standard normal approximation with several transformations that may give an accurate normal approximation even with small sample sizes. In numerical evaluations via simulations, some of the proposed methods provided more accurate results, and the empirical power of the proposed method with the arcsine square-root transformation tended to be closer to a prescribed power than the other transformations. These results were supported when methods were applied to data from three clinical trials.     

Asymptotic properties of the inhomogeneous Lotka‐Von Foerster system

In this paper, we investigate an extension of the multistate stable population model, which makes allowances for migration. The model is formulated as an inhomogeneous system of first order partial differential equations with integral boundary conditions. First, we construct its classical solution. Next, we reformulate the system as an abstract inhomogeneous Cauchy problem on a Banach space, and give its mild solution by using the population semigroup. Our main purpose is to investigate the asymptotic behavior of the mild solution.