Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER HEIDELBERG  

An empirical Bayes method to select basis functions and knots in multivariate adaptive regression spline (MARS) is proposed, which takes both advantages of frequentist model selection approaches and Bayesian approaches. A penalized likelihood is maximized to estimate regression coefficients for selected basis functions, and an approximated marginal likelihood is maximized to select knots and variables involved in basis functions. Moreover, the Akaike Bayes information criterion (ABIC) is used to determine the number of basis functions. It is shown that the proposed method gives estimation of regression structure that is relatively parsimonious and more stable for some example data sets.

DOI： 10.1007/s00180-007-0075-7

Web of Science

researchmap

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Applied Statistics  

In nonparametric regression models, the requirements of homoscedasticity and so on are implicitly assumed, which leads to poor estimation of regression functions. A power weighted smoothing spline (PWSS) model, whose objective is to diagnose homoscedasticity as well as to estimate unknown nonlinear regression structure, is assumed. The responses in an additive regression model are power-transformed, and then their variances after transformation are assumed to be constant. Smoothing splines are obtained as estimated functions by maximizing the penalized likelihood, and a reweighted version of a backfitting algorithm is constructed. A power-transformation parameter and smoothing parameters, which control smoothness of the functions, are estimated by maximizing the marginal likelihood, based on Bayesian approaches to smoothing splines. A form of the marginal likelihood, which yields comparatively easy computation, is derived using the property that smoothing splines are the best linear unbiased predictor of a linear mixed model. Examination of some data sets from the literature and a simulation experiment show that the power transformation estimated with the PWSS model attains homoscedasticity while taking nonlinear structure into account.

DOI： 10.5023/jappstat.33.27

CiNii Article

CiNii Books

researchmap

Other Link： https://jlc.jst.go.jp/DN/JALC/00242632045?from=CiNii

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

researchmap

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MARCEL DEKKER INC  

Maximum penalized likelihood estimation is applied in non(semi)-parametric regression problems, and enables us exploratory identification and diagnostics of nonlinear regression relationships. The smoothing parameter lambda controls trade-off between the smoothness and the goodness-of-fit oof a function. The method of cross-validation is used for selecting lambda, but the generalized cross-validation, which is based on the squared error criterion, shows bad behavior in non-normal distribution and can not often select reasonable lambda. The purpose of this study is to propose a method which gives more suitable lambda and to evaluate the performance of it.
A method of simple calculation for the delete-one estimates in the likelihood-based cross-validation (LCV) score is described. A score of similar form to the Akaike information criterion (AIC) is also derived. The proposed scores are compared with the ones of standard procedures by using data sets in literatures. Simulations are performed to compare the patterns of selecting lambda and overall goodness-of-fit and to evaluate the effects of some factors.
The LCV scares by the simple calculation provide good approximations to the exact one if lambda is not extremely small. Furthermore the LCV scores by the simple calculation have little risk of choosing extremely small lambda and make it possible to select lambda adaptively. They have the effect of reducing the bias of estimates and provide better performance in the sense of overall goodness of-fit. These scores are useful especially in the case of small sample size and in the case of binary logistic regression.

Web of Science

researchmap

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Computational Statistics  

In maximum penalized likelihood estimation, approaches of cross-validation (CV) are often useful in selecting a smoothing parameter. The CV score based on squared-error criterion behaves more badly than the likelihood-based score. However, it is expensive to calculate the likelihood-based score. Hence we propose a method for simple calculation of this score. The simple calculation is derived as an analogue of the deletion lemma in ordinary or penalized least squares, and is shown to be related to the one-step approximation to the estimates of parameters for the Newton-Raphson method. Our method is applied to binary data from some case studies in the context of logistic regression. It is illustrated that the simple calculation method well behaves and gives a good approximation to the likelihood-based score calculated by the delete-one method,

DOI： 10.5183/jjscs1988.10.27

CiNii Article

CiNii Books

researchmap

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

CiNii Article

researchmap

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IOP Publishing  

Abstract

Spatial clustering with spatially varying coefficient models is useful for determining the region with common effects of variables in spatial data. This study focuses on selecting the optimum tuning parameter of the generalized lasso for clustering with the spatially varying coefficient model. The k-fold cross-validation (CV) may fail to split spatial data into a training set and a testing set, if a region contains only a few observations. Moreover, the k-fold CV is known to give a biased estimate of the out-of-sample prediction error. Therefore, we investigated the performance of approximate leave-one-out cross-validation (ALOCV) in comparison with k-fold CV for selecting the tuning parameter in a simulation study on 2-dimensional grid. The ALOCV yielded smaller error than k-fold CV and could detect edges with differences shrunk by generalized lasso appropriately. Then, the ALOCV for selecting the optimum tuning parameter of the generalized lasso in fitting the spatially varying coefficient model is applied to the Chicago crime data. The result of selection by ALOCV was in accordance with the conclusion suggested in the preceding literature. Clustering into regions in advance for making k-fold CV feasible may lead to a wrong result of clustering with a spatially varying coefficient model.

DOI： 10.1088/1755-1315/950/1/012093

researchmap

Other Link： https://iopscience.iop.org/article/10.1088/1755-1315/950/1/012093/pdf

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

<title>ABSTRACT</title>
Streptomyces incarnatus NRRL8089 produces the antiviral, antifungal, antiprotozoal nucleoside antibiotic sinefungin. To enhance sinefungin production, multiple mutations were introduced to the rpoB gene encoding RNA polymerase (RNAP) β-subunit at the target residues, D447, S453, H457, and R460. Sparse regression analysis using elastic-net lasso-ridge penalties on previously reported H457X mutations identified a numeric parameter set, which suggested that H457R/Y/F may cause production enhancement. H457R/R460C mutation successfully enhanced the sinefungin production by 3-fold, while other groups of mutations, such as D447G/R460C or D447G/H457Y, made moderate or even negative effects. To identify why the rif cluster residues have diverse effects on sinefungin production, an RNAP/DNA/mRNA complex model was constructed by homology modeling and molecular dynamics simulation. The 4 residues were located near the mRNA strand. Density functional theory–based calculation suggested that D447, H457, and R460 are in direct contact with ribonucleotide, and partially positive charges are induced by negatively charged chain of mRNA.

DOI： 10.1093/bbb/zbab011

PubMed

researchmap

Other Link： http://academic.oup.com/bbb/article-pdf/85/5/1275/37329090/zbab011.pdf

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IOP Publishing  

DOI： 10.1088/1742-6596/1863/1/012014

researchmap

Other Link： https://iopscience.iop.org/article/10.1088/1742-6596/1863/1/012014/pdf

Language：Japanese   Publishing type：Research paper (scientific journal)  

J-GLOBAL

researchmap

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/jrsm.1119

Web of Science

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Computational Statistics  

Random Forest (RF) is one of tree-structured approaches, such as classification and regression trees, involved in ensemble learning method to predict outcome more precisely. In this paper we proposed an adjusted RF based on lasso (lasso-RF) for further predictive performance in regression analysis. In particular, we integrated lasso which use one of shrinkage estimators into the tree-structured model of RF. Practically we carried out two case studies and a small scale simulation with factors which influence prediction of outcome such as sample size {100, 200, 400}, methods {RF, lasso-RF} and bootstrap re-sampling times {100, 200, 400}, and evaluated predictive performance. Our case studies suggested that lasso-RF decreased the mean squared errors between true and estimate outcomes less than original RF. By our simulation study, we evaluated influencing factors on the mean squared errors based on analysis of variance with above three factors and their interaction, and showed that the factor of methods as fixed effect was significant at level 0.05 with proportion of variation 29.2%.

DOI： 10.20551/jscswabun.26.1_17

CiNii Article

CiNii Books

researchmap

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.5183/jjscs.1212001_203

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Computational Statistics  

In the framework of Bayesian approach, though we can select various prior distributions according to the situations, many models which should be evaluated exist. However, we think that the diagnosis methods for these models have proper diagnostic situations. In this paper, we consider two diagnostic methods that focus on prediction: the Bayesian predictive information criterion (BPIC) and the prior and posterior predictive checking approach (PCA) and conduct some simulations for the purpose of clarifying the feature of these methods and suggesting the effective diagnostic situations. As the results, regardless of whether the prior mean was true or not, BPIC showed the low values on the occasion with strong prior information, but PCA showed the high predictive checking probabilities on the occasion with weak prior information. So, it might happen that the model with non-true prior mean was selected by simulation setting. To select a proper model, it is necessary to find the performance of the model diagnoses in the situation before model evaluations.

DOI： 10.20551/jscswabun.25.1_1

CiNii Article

CiNii Books

researchmap

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

researchmap

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

DOI： 10.1371/journal.pntd.0000263

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)  

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)  

researchmap

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Computational Statistics  

In the drug development process, it is essential to assess the relationship (mechanism) between dose and response of a biological system following drug administration. This mechanism is known as the "dose-response relationship". Then, dose-response relationships are often evaluated based on a monotonic hypothesis. However, in practice, we may often encounter non-monotonic dose-response relationships, such as the umbrella relationship, which then makes interpretation of the relationships somewhat problematic. In this paper, to assess such umbrella dose-response relationships, the cumulative dose logit model is proposed and applied to an example. To evaluate properties of this model, some Monte-Carlo studies are performed. The results of the cumulative dose logit model are compared with those of the quadratic logit model. This indicates that the cumulative dose logit model provides more stable estimates than the quadratic logit model in estimating the maximum effective dose. It is suggested that the cumulative dose logit model is appropriate for assessing non-monotonic dose-response relationships.

CiNii Article

CiNii Books

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Computational Statistics  

In this article, we discuss methods to construct confidence bands for regression function, especially to construct uniform width-confidence bands and trapezoidal confidence bands in a finite interval of interest. With case studies and numerical experiments, we examined the coverage probability and the efficiency (area ratio) of these confidence bands. From a viewpoint of the coverage probability, it is reasonable to use the uniform width- confidence bands (Gafarian bands) and trapezoidal confidence bands (Bowden-Graybill bands) for the finite interval of interest, rather than to use the Working-Hotelling bands. In particular, the use of these confidence bands can be emphasized when a length of the interval of interest is smaller than that of the observation interval. Moreover, from the examination about efficiency, it is strongly recommended to use the trapezoidal confidence bands when the interval of interest is near the relative ends of the observation interval.

DOI： 10.20551/jscswabun.14.1_29

CiNii Article

CiNii Books

researchmap

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society of Applied Statistics  

DOI： 10.5023/jappstat.26.135

CiNii Article

researchmap

Authorship：Lead author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (bulletin of university, research institution)  

researchmap

Language：Japanese  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：Japanese  

researchmap

J-GLOBAL

researchmap

Language：Japanese  

researchmap

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：先端医学社  

CiNii Article

CiNii Books

researchmap

Other Link： http://search.jamas.or.jp/link/ui/2016087928

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：日本計算機統計学会  

DOI： 10.20551/jscssymo.27.0_131

CiNii Article

CiNii Books

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

Network Management System (NMS) is a service that employs a variety of tools, applications, and devices to assist network administrators on monitoring and maintaining network. Keeping the network in high quality of service is the main purpose of NMS. This paper proposed a method to solve the network problem by making a prediction of failure based on network-data behavior. The prediction represented by conditional probability generated by Bayesian network. Bayesian network is a probability graphical model for representing the probabilistic relationship among a large number of variables and doing probabilistic inference with those variables. In order to describe how the prediction works, we discuss the prediction result by simulation on network congestion.

Scopus

researchmap

Publishing type：Research paper, summary (national, other academic conference)  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：日本計算機統計学会  

DOI： 10.20551/jscstaikai.20.0_165

CiNii Article

CiNii Books

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Publishing type：Meeting report  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：日本計算機統計学会  

DOI： 10.20551/jscssymo.16.0_101

CiNii Article

CiNii Books

researchmap

Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Publishing type：Article, review, commentary, editorial, etc. (bulletin of university, research institution)  

researchmap

Publishing type：Research paper, summary (national, other academic conference)  

researchmap

Publishing type：Article, review, commentary, editorial, etc. (bulletin of university, research institution)  

researchmap

Publishing type：Research paper, summary (national, other academic conference)  

researchmap

Publishing type：Article, review, commentary, editorial, etc. (bulletin of university, research institution)  

researchmap

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：日本計算機統計学会  

DOI： 10.20551/jscssymo.12.0_49

CiNii Article

CiNii Books

researchmap

Language：English   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

researchmap

Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)   Publisher：日本計算機統計学会  

DOI： 10.20551/jscssymo.10.0_20

CiNii Article

CiNii Books

researchmap

Language：Japanese   Presentation type：Oral presentation (general)  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Language：English   Presentation type：Oral presentation (invited, special)  

researchmap

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Language：English   Presentation type：Oral presentation (invited, special)  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Language：English   Presentation type：Oral presentation (invited, special)  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Oral presentation (general)  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

researchmap

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Presentation type：Oral presentation (general)  

researchmap

Language：English   Presentation type：Oral presentation (general)  

researchmap

Presentation type：Oral presentation (general)  

researchmap

Presentation type：Oral presentation (invited, special)  

researchmap

Type：Academic society, research group, etc. 

researchmap

Type：Academic society, research group, etc. 

researchmap

researchmap

Type：Academic society, research group, etc. 

researchmap

Type：Academic society, research group, etc. 

researchmap

Type：Academic society, research group, etc. 

researchmap

Type：Competition, symposium, etc. 

researchmap

Type：Peer review 

researchmap

Type：Competition, symposium, etc. 

researchmap