# Annals of the Institute of Statistical Mathematics (Ann Inst Stat Math )

Publisher: Tōkei-Sūri-Kenkyūsho (Tōkyō), Springer Verlag

## Description

Annals of the Institute of Statistical Mathematics (AISM) provides an international forum for open communication among statisticians and researchers working with the common purpose of advancing human knowledge through the development of the science and technology of statistics. AISM will publish broadest possible coverage of statistical papers of the highest quality. The emphasis will be placed on the publication of papers related to: (a) the establishment of new areas of application; (b) the development of new procedures and algorithms; (c) the development of unifying theories; (d) the analysis and improvement of existing procedures and theories; and the communication of empirical findings supported by real data. In addition to papers by professional statisticians contributions are also published by authors working in various fields of application. Authors discussing applications are encouraged to contribute a complete set of data used in their papers to the AISM Data Library. The Institute of Statistical Mathematics will distribute it upon request from readers (see p. 405 and 606 Vol. 43 No. 3 1991). The final objective of AISM is to contribute to the advancement of statistics as the science of human handling of information to cope with uncertainties. Special emphasis will thus be placed on the publication of papers that will eventually lead to significant improvements in the practice of statistics.

• Impact factor
0.74
• 5-year impact
0.84
• Cited half-life
0.00
• Immediacy index
0.19
• Eigenfactor
0.00
• Article influence
0.86
• Website
Annals of the Institute of Statistical Mathematics website
• ISSN
1572-9052
• OCLC
162265965
• Material type
Internet resource
• Document type
Internet Resource, Computer File, Journal / Magazine / Newspaper

## Publisher details

• Pre-print
• Author can archive a pre-print version
• Post-print
• Author can archive a post-print version
• Conditions
• Authors own final version only can be archived
• Publisher's version/PDF cannot be used
• On author's website or institutional repository
• On funders designated website/repository after 12 months at the funders request or as a result of legal obligation
• Published source must be acknowledged
• Must link to publisher version
• Set phrase to accompany link to published version (The original publication is available at www.springerlink.com)
• Articles in some journals can be made Open Access on payment of additional charge
• Classification
​ green

## Publications in this journal

• Source
##### Article: Hazard Function Estimation with Cause-of-Death Data Missing at Random.
[hide abstract]
ABSTRACT: Hazard function estimation is an important part of survival analysis. Interest often centers on estimating the hazard function associated with a particular cause of death. We propose three nonparametric kernel estimators for the hazard function, all of which are appropriate when death times are subject to random censorship and censoring indicators can be missing at random. Specifically, we present a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators are uniformly strongly consistent and asymptotically normal. We derive asymptotic representations of the mean squared error and the mean integrated squared error for these estimators and we discuss a data-driven bandwidth selection method. A simulation study, conducted to assess finite sample behavior, demonstrates that the proposed hazard estimators perform relatively well. We illustrate our methods with an analysis of some vascular disease data.
Annals of the Institute of Statistical Mathematics 04/2012; 64(2):415-438.
• Source
##### Article: Combining models in longitudinal data analysis
[hide abstract]
ABSTRACT: Model selection uncertainty in longitudinal data analysis is often much more serious than that in simpler regression settings, which challenges the validity of drawing conclusions based on a single selected model when model selection uncertainty is high. We advocate the use of appropriate model selection diagnostics to formally assess the degree of uncertainty in variable/model selection as well as in estimating a quantity of interest. We propose a model combining method with its theoretical properties examined. Simulations and real data examples demonstrate its advantage over popular model selection methods.
Annals of the Institute of Statistical Mathematics 01/2012; 64(2):233-254.
• ##### Article: Statistical modeling for discrete patterns in a sequence of exchangeable trials
[hide abstract]
ABSTRACT: This paper proposes a new method for constructing a sequence of infinitely exchangeable uniform random variables on the unit interval. For constructing the sequence, we utilize a Pólya urn partially. The resulting exchangeable sequence depends on the initial numbers of balls of the Pólya urn. We also derive the de Finetti measure for the exchangeable sequence. For an arbitrarily given one-dimensional distribution function, we generate sequences of exchangeable random variables with the one-dimensional marginal distribution by transforming the exchangeable uniform sequences with the inverse function of the distribution function. Among them we mainly investigate sequences of exchangeable discrete random variables. They differ from the well-known exchangeable sequence generated only by the Pólya urn scheme. Some examples are also given as applications of the results to exact distributions of some statistics based on sequences of exchangeable trials. Further, from the above exchangeable uniform sequence we construct partial or Markov exchangeable sequences. We also provide numerical examples of statistical inference based on the exchangeable and Markov exchangeable sequences.
Annals of the Institute of Statistical Mathematics 01/2012; 64(3):633-655.
• ##### Article: Estimators for the binomial distribution that dominate the MLE in terms of Kullback–Leibler risk
[hide abstract]
ABSTRACT: Estimators based on the mode are introduced and shown empirically to have smaller Kullback–Leibler risk than the maximum likelihood estimator. For one of these, the midpoint modal estimator (MME), we prove the Kullback–Leibler risk is below $${\frac{1}{2}}$$ while for the MLE the risk is above $${\frac{1}{2}}$$ for a wide range of success probabilities that approaches the unit interval as the sample size grows to infinity. The MME is related to the mean of Fisher’s Fiducial estimator and to the rule of succession for Jefferey’s noninformative prior.
Annals of the Institute of Statistical Mathematics 01/2012; 64(2):359-371.
• ##### Article: Directional dependence in multivariate distributions
[hide abstract]
ABSTRACT: In this paper, we develop some coefficients which can be used to detect dependence in multivariate distributions not detected by several known measures of multivariate association. Several examples illustrate our results.
Annals of the Institute of Statistical Mathematics 01/2012; 64(3):677-685.
• ##### Article: Bayesian estimation of a covariance matrix with flexible prior specification
[hide abstract]
ABSTRACT: Bayesian analysis for a covariance structure has been in use for decades. The commonly adopted Bayesian setup involves the conjugate inverse Wishart prior specification for the covariance matrix. Here we depart from this approach and adopt a novel prior specification by considering a multivariate normal prior for the elements of the matrix logarithm of the covariance structure. This specification allows for a richer class of prior distributions for the covariance structure with respect to strength of beliefs in prior location hyperparameters and the added ability to model potential correlation amongst the covariance structure. We provide three computational methods for calculating the posterior moment of the covariance matrix. The moments of interest are calculated based upon computational results via Importance sampling, Laplacian approximation and Markov Chain Monte Carlo/Metropolis–Hastings techniques. As a particular application of the proposed technique we investigate educational test score data from the project talent data set.
Annals of the Institute of Statistical Mathematics 01/2012; 64(2):319-342.
• ##### Article: Constrained nonparametric estimation of the mean and the CDF using ranked-set sampling with a covariate
[hide abstract]
ABSTRACT: Ranked-set sampling (RSS) and judgment post-stratification (JPS) are related schemes in which more efficient statistical inference is obtained by creating a stratification based on ranking information. The rankings may be completely subjective, or they may be based on values of a covariate. Recent work has shown that regardless of how the rankings are done, the in-stratum cumulative distribution functions (CDFs) must satisfy certain constraints, and we show here that if the rankings are done according to a covariate, then tighter constraints must hold. We also show that under a mild stochastic ordering assumption, still tighter constraints must hold. Taking advantage of these new constraints leads to improved small-sample estimates of the in-stratum CDFs in all RSS and JPS settings. For JPS, the new constraints also lead to improved estimates of the overall CDF and the population mean.
Annals of the Institute of Statistical Mathematics 01/2012; 64(2):439-456.
• Source
##### Article: Representations of efficient score for coarse data problems based on Neumann series expansion.
[hide abstract]
ABSTRACT: We derive new representations of the efficient score for coarse data problems based on Neumann series expansion. The representations can be applied to both ignorable and nonignorable coarse data. An approximation to the new representation may be used for computing locally efficient scores in such problems. We show that many of the successive approximation approaches to the computation of the locally efficient score proposed in the literature for coarse data problems can be derived as special cases of the representations. In addition, the representations lead to new algorithms for computing the locally efficient scores for the coarse data problems.
Annals of the Institute of Statistical Mathematics 06/2011; 63(3):497-509.
• Source
##### Article: Density Estimation with Replicate Heteroscedastic Measurements.
[hide abstract]
ABSTRACT: We present a deconvolution estimator for the density function of a random variable from a set of independent replicate measurements. We assume that measurements are made with normally distributed errors having unknown and possibly heterogeneous variances. The estimator generalizes the deconvoluting kernel density estimator of Stefanski and Carroll (1990), with error variances estimated from the replicate observations. We derive expressions for the integrated mean squared error and examine its rate of convergence as n → ∞ and the number of replicates is fixed. We investigate the finite-sample performance of the estimator through a simulation study and an application to real data.
Annals of the Institute of Statistical Mathematics 02/2011; 63(1):81-99.
• Source
##### Article: Efficiency of profile likelihood in semi-parametric models
[hide abstract]
ABSTRACT: Profile likelihood is a popular method of estimation in the presence of an infinite-dimensional nuisance parameter, as the method reduces the infinite-dimensional estimation problem to a finite-dimensional one. In this paper we investigate the efficiency of a semi-parametric maximum likelihood estimator based on the profile likelihood. By introducing a new parametrization, we improve on the seminal work of Murphy and van der Vaart (J Am Stat Assoc, 95: 449–485, 2000): our improvement establishes the efficiency of the estimator through the direct quadratic expansion of the profile likelihood, which requires fewer assumptions. To illustrate the method an application to two-phase outcome-dependent sampling design is given.
Annals of the Institute of Statistical Mathematics 01/2011; 63(6):1247-1275.
• Source
##### Article: Erratum to: A Wald-type variance estimation for the nonparametric distribution estimators for doubly censored data
[hide abstract]
ABSTRACT: We discuss the variance estimation for the nonparametric distribution estimator for doubly censored data. We first provide another view of Kuhn–Tucker’s conditions to construct the profile likelihood, and lead a Newton–Raphson algorithm as an optimization technique unlike the EM algorithm. The main proposal is an iteration-free Wald-type variance estimate based on the chain rule of differentiating conditions to construct the profile likelihood, which generalizes the variance formula in only right- or left-censored data. In this estimation procedure, we overcome some difficulties caused in directly applying Turnbull’s formula to large samples and avoid a load with computationally heavy iterations, such as solving the Fredholm equations, computing the profile likelihood ratio or using the bootstrap. Also, we establish the consistency of the formulated Wald-type variance estimator. In addition, simulation studies are performed to investigate the properties of the Wald-type variance estimates in finite samples in comparison with those from the profile likelihood ratio.
Annals of the Institute of Statistical Mathematics 01/2011; 63(4):671-674.
• Source
##### Article: Latent Class Analysis Variable Selection.
[hide abstract]
ABSTRACT: We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNPs.
Annals of the Institute of Statistical Mathematics 02/2010; 62(1):11-35.
• Source
##### Article: Finding market structure by sales count dynamics—Multivariate structural time series models with hierarchical structure for count data—
[hide abstract]
ABSTRACT: This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.
Annals of the Institute of Statistical Mathematics 01/2010; 62(1):91-107.

#### Related Journals

• ##### Quintessence international (Berlin, Germany: 1985)

Quintessence Publishing

ISSN: 1936-7163, Impact factor: 0.64

• ##### IEEJ Transactions on Electrical and Electronic Engineering

John Wiley & Sons

ISSN: 1931-4981, Impact factor: 0.34

• ##### Journal of atherosclerosis and thrombosis

ISSN: 1880-3873, Impact factor: 2.93

• ##### Computers in biology and medicine

Elsevier

ISSN: 1879-0534, Impact factor: 1.27

• ##### Journal of neuroscience methods

Elsevier

ISSN: 1872-678X, Impact factor: 2.3

• ##### Cancer biology & therapy

Landes Bioscience

ISSN: 1555-8576, Impact factor: 3.29

• ##### Alzheimer disease and associated disorders

ISSN: 1546-4156, Impact factor: 2.88

• ##### International Journal of Green Energy

Association of Energy Engineers,...

ISSN: 1543-5075, Impact factor: 2.07