JOURNAL OF THE JAPAN STATISTICAL SOCIETY
Online ISSN : 1348-6365
Print ISSN : 1882-2754
Volume 39 , Issue 1
Showing 1-7 articles out of 7 articles from the selected issue
Articles
• B. Re. Victorbabu, V. Vasundharadevi
2009 Volume 39 Issue 1 Pages 1-14
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
In this paper, the efficiencies of various second order response surface designs, like second order rotatable designs (SORD), second order slope rotatable designs (SOSRD), SORDs with an equi-spaced doses design, and SOSRDs with an equi-spaced doses design using symmetrical unequal block arrangements (SUBA) with two unequal block sizes, are studied for the estimation of responses and slopes at different points (central, axial, cube corner points) on second order response surface designs. The study is done because in some cases a SORD, and SOSRD constructed by using a SUBA with two unequal block sizes have fewer design points compared to those constructed by other methods.
• Subir Ghosh, Jesús López–Fidalgo, Rupam Pal
2009 Volume 39 Issue 1 Pages 15-28
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
The problem of discriminating between two competing simple linear regression models MI and MII is discussed in this paper. Model MI is nested within model MII with a common linear term being present in both models with respect to an explanatory variable and an additional quadratic term with respect to the same explanatory variable being present in MII. The first criterion function for discrimination between MI and MII is in terms of minimizing the variance of the least squares estimated coefficient of the quadratic term. A lower bound is obtained for this variance. Two designs are presented satisfying this lower bound in two experimental regions. New criterion functions for discriminating between MI and MII are given based on maximizing the difference between the fitted values of n observations under MI and MII, and maximizing the difference between the predicted values under MI and MII. Several results are obtained for demonstrating the performances of these designs under two new criterion functions. We also present five general classes of designs and demonstrate their sharp relative performances with respect to our criterion functions. Some results for discriminating between two competing general linear models MIII and MIV are also given.
• Akikuni Matsumoto, Hisayuki Hara, Kazumitsu Nawata
2009 Volume 39 Issue 1 Pages 29-47
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
In this paper, we analyze the dynamic labor demand structure of large Japanese firms. We propose a new dynamic model which explicitly considers the asymmetric behavior of the firms between decreasing and increasing regimes. The model modifies the ordinary partial adjustment and switching cost models. The model is a Tobit-type model; that is, the employment strategies and desired levels of labor are determined by latent variables. We estimate the model using the data augmentation algorithm, which is a Bayesian simulation method. We apply the model to the panel data constructed from financial reports of large Japanese manufacturing firms. When asymmetric adjustment costs are included in the model, we find that: i) the hiring cost does not become lower even if lay-offs and dismissals are easier, and ii) employment strategies differ among the industrial sectors even if their cost structures are similar.
• Hiroki Masuda
2009 Volume 39 Issue 1 Pages 49-75
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
Consider a real-valued non-Gaussian stable Lévy process X such that $\mathcal{L}$(Xt-γt)=Sα(t1/ασ), and suppose that we observe a discrete-time sample (Xihn)ni=0. Under the condition hn→0 at an appropriate rate, the corresponding statistical experiments governed by the parameter θ=(α,σ,γ) exhibit the LAN property at the unusual rate of convergence diagn log(1/hn),√n, √n hn1-1/α, but the Fisher information matrix is constantly singular as soon as both α and σ are unknown. This implies that the standard asymptotic behavior of the maximum likelihood estimator breaks down, and also that it is in no way obvious whether or not existing results concerning estimators of the stable law in the usual case where hn≡ h>0 can maintain the same asymptotic behaviors. In this note we will provide easily computable full-joint estimators of the parameters, which possess asymptotic normality with a finite and nondegenerate asymptotic covariance matrix, thereby enabling us to construct a joint confidence region of the three parameters: the rate of convergence of our estimators of θ is diag(√n,√n,√n hn1-1/α). Especially, we clarify that a suitable sample-median type statistic $\hat{γn}$ serves as a rate-efficient estimator of the location γ, and that our procedure of estimating the remaining two parameters is not asymptotically influenced by plugging in $\hat{γn}$, even if the convergence rate of <$\hat{γn}$ is slower than the other two (namely, even if α∈(1,2)). Finite-sample behaviors of our estimators are investigated through several simulation experiments.
• Hirohisa Kishino
2009 Volume 39 Issue 1 Pages 77-88
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
In this paper, we consider the estimation problem of the population density of organisms in an area. We formulate this biometric problem as a variant of the sequential estimation problem of the intensity of the Poisson process and propose a method for simultaneously optimizing both the decision of an observation subarea and the estimation. By request from the application side, we define the equivariance of a procedure composed of a decision rule of an observation subarea and an estimator of population density under the scale transformations of an observation area, and then construct a scale-equivariant procedure. As a result of using the invariance principle, we utilize the framework of statistical decision theory, not the conventional framework of sequential estimation using asymptotic methods, and discuss the admissibility and minimaxity of our proposed procedure.
• Hiroaki Uehara, Masakazu Jimbo
2009 Volume 39 Issue 1 Pages 89-109
Published: July 31, 2009
Released: January 31, 2010
JOURNALS FREE ACCESS
We describe an algorithm for extracting as much information as possible from pooling experiments for library screening based on the concave-convex procedure (CCCP). Called the CCCP pool result decoder (CCPD), it is a positive clone detecting algorithm. Its performance is compared, by simulation, with the Bayesian network pool result decoder (BNPD) proposed by Uehara and Jimbo and the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996.