Annals of the Institute of Statistical Mathematics Journal Impact Factor & Information

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

Journal 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.

Current impact factor: 0.82

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 0.82
2013 Impact Factor 0.661
2012 Impact Factor 0.736
2011 Impact Factor 0.857
2010 Impact Factor 0.966
2009 Impact Factor 0.61
2008 Impact Factor 0.565
2007 Impact Factor 0.38
2006 Impact Factor 0.355
2005 Impact Factor 0.376
2004 Impact Factor 0.369
2003 Impact Factor 0.468
2002 Impact Factor 0.386
2001 Impact Factor 0.214
2000 Impact Factor 0.253
1999 Impact Factor 0.37
1998 Impact Factor 0.216
1997 Impact Factor 0.368
1996 Impact Factor 0.3
1995 Impact Factor 0.297
1994 Impact Factor 0.175
1993 Impact Factor 0.266
1992 Impact Factor 0.396

Impact factor over time

Impact factor

Additional details

5-year impact 0.81
Cited half-life >10.0
Immediacy index 0.21
Eigenfactor 0.00
Article influence 0.73
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

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification

Publications in this journal

  • Annals of the Institute of Statistical Mathematics 09/2014; DOI:10.1007/s10463-014-0486-5
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    ABSTRACT: Although the parameters in a finite mixture model are unidentifiable, there is a form of local identifiability guaranteeing the existence of the identifiable parameter regions. To verify its existence, practitioners use the Fisher information on the estimated parameters. However, there exist model/data situations where local identifiability based on Fisher information does not correspond to that based on the likelihood. In this paper, we propose a method to empirically measure degree of local identifiability on the estimated parameters, empirical identifiability, based on one’s ability to construct an identifiable likelihood set. From a detailed topological study of the likelihood region, we show that for any given data set and mixture model, there typically exists limited range of confidence levels where the likelihood region has a natural partition into identifiable subsets. At confidence levels that are too high, there is no natural way to use the likelihood to resolve the identifiability problem.
    Annals of the Institute of Statistical Mathematics 07/2014; 67(4). DOI:10.1007/s10463-014-0474-9
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    ABSTRACT: We develop particle Gibbs samplers for static-parameter estimation in discretely observed piecewise deterministic process (PDPs). PDPs are stochastic processes that jump randomly at a countable number of stopping times but otherwise evolve deterministically in continuous time. A sequential Monte Carlo (SMC) sampler for filtering in PDPs has recently been proposed. We first provide new insight into the consequences of an approximation inherent within that algorithm. We then derive a new representation of the algorithm. It simplifies ensuring that the importance weights exist and also allows the use of variance-reduction techniques known as backward and ancestor sampling. Finally, we propose a novel Gibbs step that improves mixing in particle Gibbs samplers whose SMC algorithms make use of large collections of auxiliary variables, such as many instances of SMC samplers. We provide a comparison between the two particle Gibbs samplers for PDPs developed in this paper. Simulation results indicate that they can outperform reversible-jump MCMC approaches.
    Annals of the Institute of Statistical Mathematics 06/2014; 66(3-3):577-609. DOI:10.1007/s10463-014-0455-z
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    ABSTRACT: The majority of modelling and inference regarding Hidden Markov Models (HMMs) assumes that the number of underlying states is known a priori. However, this is often not the case and thus determining the appropriate number of underlying states for a HMM is of considerable interest. This paper proposes the use of a parallel sequential Monte Carlo samplers framework to approximate the posterior distribution of the number of states. This requires no additional computational effort if approximating parameter posteriors conditioned on the number of states is also necessary. The proposed strategy is evaluated on a comprehensive set of simulated data and shown to outperform the state of the art in this area: although the approach is simple, it provides good performance by fully exploiting the particular structure of the problem. An application to business cycle analysis is also presented.
    Annals of the Institute of Statistical Mathematics 06/2014; 66(3-3):553-575. DOI:10.1007/s10463-014-0450-4
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    ABSTRACT: Mean marks form a versatile toolbox in the analysis of marked point processes (MPPs). For ergodic processes, their definition is straightforward and practical application is well established. In the stationary non-ergodic case, though, different definitions of mark averages are possible and might be practically relevant. In this paper, the classical definition of mean marks is compared to a set of new characteristics for non-ergodic MPPs, which stand out due to the weighting of ergodicity classes. Another weighting can be introduced on the single-point level via weights given by the marks themselves. These intrinsically given weights and the weighting of ergodicity classes are closely related to each other meaning that for suitable choices of weights, a mean mark characteristic can be expressed in either way. Estimators for the different definitions of mean marks are discussed and their consistency and asymptotic normality are shown under certain conditions.
    Annals of the Institute of Statistical Mathematics 01/2014;
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    ABSTRACT: This paper introduces a new family of local density separations for assessing robustness of finite-dimensional Bayesian posterior inferences with respect to their priors. Unlike for their global equivalents, under these novel separations posterior robustness is recovered even when the functioning posterior converges to a defective distribution, irrespectively of whether the prior densities are grossly misspecified and of the form and the validity of the assumed data sampling distribution. For exponential family models, the local density separations are shown to form the basis of a weak topology closely linked to the Euclidean metric on the natural parameters. In general, the local separations are shown to measure relative roughness of the prior distribution with respect to its corresponding posterior and provide explicit bounds for the total variation distance between an approximating posterior density to a genuine posterior. We illustrate the application of these bounds for assessing robustness of the posterior inferences for a dynamic time series model of blood glucose concentration in diabetes mellitus patients with respect to alternative prior specifications.
    Annals of the Institute of Statistical Mathematics 06/2012; 64(3):495-519. DOI:10.1007/s10463-011-0334-9
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    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 06/2012; 64(3):677-685. DOI:10.1007/s10463-011-0329-6
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    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 04/2012; 64(2):359-371. DOI:10.1007/s10463-010-0316-3
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    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 04/2012; 64(2):439-456. DOI:10.1007/s10463-011-0326-9