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  • 1
    Online Resource
    Online Resource
    Adjacency-based regularization for partially ranked data with non-ignorable missing
    Elsevier BV ; 2020
    In:  Computational Statistics & Data Analysis Vol. 145 ( 2020-05), p. 106905-
    Details
    In: Computational Statistics & Data Analysis, Elsevier BV, Vol. 145 ( 2020-05), p. 106905-
    Type of Medium: Online Resource
    ISSN: 0167-9473
    URL: Article
    DOI: 10.1016/j.csda.2019.106905
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1478763-5
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  • 2
    Online Resource
    Online Resource
    Bayesian Predictive Distribution with Right Invariant Priors
    SAGE Publications ; 2002
    In:  Calcutta Statistical Association Bulletin Vol. 52, No. 1-4 ( 2002-03), p. 171-180
    Details
    In: Calcutta Statistical Association Bulletin, SAGE Publications, Vol. 52, No. 1-4 ( 2002-03), p. 171-180
    Abstract: There exist many examples where Bayesian predictive distributions are more appropriate than plug-in distributions. When we use Bayesian procedure, the choice of prior distributions is a serious problem. Non-informative prior distributions or vague prior distributions are often adopted to construct Bayesian predictive distributions. There exist many studies on the relation between priors and Bayes estimators. Here, we investigate the corresponding relation between priors and Bayesian predictive distributions. We adopt Kullback-Leibler divergence from the true distribution to a predictive distribution as a loss function . We consider invariant predictive distributions. When a model has a group structure, the right invariant measure is often recommended as a noninformative prior. It is shown that Bayesian predictive distributions based on right invariant measures are the best invariant predictive distributions. We touch on shrinkage method of constructing predictive distributions.
    Type of Medium: Online Resource
    ISSN: 0008-0683 , 2456-6462
    URL: Article
    DOI: 10.1177/0008068320020508
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2002
    detail.hit.zdb_id: 2867649-X
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  • 3
    Online Resource
    Online Resource
    Time Series Decomposition into Oscillation Components and Phase Estimation
    MIT Press ; 2017
    In:  Neural Computation Vol. 29, No. 2 ( 2017-02), p. 332-367
    Details
    In: Neural Computation, MIT Press, Vol. 29, No. 2 ( 2017-02), p. 332-367
    Abstract: Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes’ method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    URL: Article
    DOI: 10.1162/NECO_a_00916
    Language: English
    Publisher: MIT Press
    Publication Date: 2017
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 4
    Online Resource
    Online Resource
    Bayesian optimal response-adaptive design for binary responses using stopping rule
    SAGE Publications ; 2018
    In:  Statistical Methods in Medical Research Vol. 27, No. 3 ( 2018-03), p. 891-904
    Details
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 27, No. 3 ( 2018-03), p. 891-904
    Abstract: Response-adaptive designs are used in phase III clinical trials to allocate a larger number of patients to the better treatment arm. Optimal designs are explored in the recent years in the context of response-adaptive designs, in the frequentist view point only. In the present paper, we propose some response-adaptive designs for two treatments based on Bayesian prediction for phase III clinical trials. Some properties are studied and numerically compared with some existing competitors. A real data set is used to illustrate the applicability of the proposed methodology where we redesign the experiment using parameters derived from the data set.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    URL: Article
    DOI: 10.1177/0962280216647210
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2001539-2
    detail.hit.zdb_id: 1136948-6
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  • 5
    Online Resource
    Online Resource
    Multivariate Time Series Decomposition into Oscillation Components
    MIT Press ; 2017
    In:  Neural Computation Vol. 29, No. 8 ( 2017-08), p. 2055-2075
    Details
    In: Neural Computation, MIT Press, Vol. 29, No. 8 ( 2017-08), p. 2055-2075
    Abstract: Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    URL: Article
    DOI: 10.1162/neco_a_00981
    Language: English
    Publisher: MIT Press
    Publication Date: 2017
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 6
    Online Resource
    Online Resource
    Correction to: Statistical modeling for temporal dominance of sensations data incorporating individual characteristics of panelists: an application to data of milk chocolate
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Food Science and Technology Vol. 59, No. 6 ( 2022-06), p. 2429-2429
    Details
    In: Journal of Food Science and Technology, Springer Science and Business Media LLC, Vol. 59, No. 6 ( 2022-06), p. 2429-2429
    Type of Medium: Online Resource
    ISSN: 0022-1155 , 0975-8402
    URL: Article
    DOI: 10.1007/s13197-021-05311-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2537738-3
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  • 7
    Online Resource
    Online Resource
    Statistical modeling for temporal dominance of sensations data incorporating individual characteristics of panelists: an application to data of milk chocolate
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Food Science and Technology Vol. 59, No. 6 ( 2022-06), p. 2420-2428
    Details
    In: Journal of Food Science and Technology, Springer Science and Business Media LLC, Vol. 59, No. 6 ( 2022-06), p. 2420-2428
    Abstract: We discuss the modeling of temporal dominance of sensations (TDS) data, time series data appearing in sensory analysis, that describe temporal changes of the dominant taste in the oral cavity. Our aims were to obtain the transition process of attributes (tastes and mouthfeels) in the oral cavity, to express the tendency of dominance durations of attributes, and to specify factors (such as sex, age, food preference, dietary habits, and sensitivity to a particular taste) affecting dominance durations, simultaneously. To achieve these aims, we propose an analysis procedure applying models based on the semi-Markov chain and the negative binomial regression, one of the generalized linear models. By using our method, we can take differences among individual panelists and dominant attributes into account. We analyzed TDS data for milk chocolate with the proposed method and verified the performance of our model compared with conventional analysis methods. We found that our proposed model outperformed conventional ones; moreover, we identified factors that have effects on dominance durations. Results of an experiment support the importance of reflecting characteristics of panelists and attributes.
    Type of Medium: Online Resource
    ISSN: 0022-1155 , 0975-8402
    URL: Article
    DOI: 10.1007/s13197-021-05260-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2537738-3
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  • 8
    Online Resource
    Online Resource
    An Estimation Procedure for Contingency Table Models Based on Nested Geometry
    The Japan Statistical Society ; 2015
    In:  JOURNAL OF THE JAPAN STATISTICAL SOCIETY Vol. 45, No. 1 ( 2015), p. 57-75
    Details
    In: JOURNAL OF THE JAPAN STATISTICAL SOCIETY, The Japan Statistical Society, Vol. 45, No. 1 ( 2015), p. 57-75
    Type of Medium: Online Resource
    ISSN: 1348-6365 , 1882-2754
    URL: Article
    DOI: 10.14490/jjss.45.57
    Language: English
    Publisher: The Japan Statistical Society
    Publication Date: 2015
    detail.hit.zdb_id: 2135659-2
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  • 9
    Online Resource
    Online Resource
    Asymptotic Properties of Bayesian Predictive Densities When the Distributions of Data and Target Variables are Different
    Institute of Mathematical Statistics ; 2015
    In:  Bayesian Analysis Vol. 10, No. 1 ( 2015-3-1)
    Details
    In: Bayesian Analysis, Institute of Mathematical Statistics, Vol. 10, No. 1 ( 2015-3-1)
    Type of Medium: Online Resource
    ISSN: 1936-0975
    URL: Article
    DOI: 10.1214/14-BA886
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2015
    detail.hit.zdb_id: 2201249-7
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  • 10
    Online Resource
    Online Resource
    Structured regularization based velocity structure estimation in local earthquake tomography for the adaptation to velocity discontinuities
    Springer Science and Business Media LLC ; 2022
    In:  Earth, Planets and Space Vol. 74, No. 1 ( 2022-03-18)
    Details
    In: Earth, Planets and Space, Springer Science and Business Media LLC, Vol. 74, No. 1 ( 2022-03-18)
    Abstract: We propose a local earthquake tomography method that applies a structured regularization technique to determine sharp changes in Earth’s seismic velocity structure using arrival time data of direct waves. Our approach focuses on the ability to better image two common features that are observed in Earth’s seismic velocity structure: sharp changes in velocities that correspond to material boundaries, such as the Conrad and Moho discontinuities; and gradual changes in velocity that are associated with pressure and temperature distributions in the crust and mantle. We employ different penalty terms in the vertical and horizontal directions to refine the earthquake tomography. We utilize a vertical-direction (depth) penalty that takes the form of the $${l}_{1}$$ l 1 -sum of the $${l}_{2}$$ l 2 -norms of the second-order differences of the horizontal units in the vertical direction. This penalty is intended to represent sharp velocity changes caused by discontinuities by creating a piecewise linear depth profile of seismic velocity. We set a horizontal-direction penalty term on the basis of the $${l}_{2}$$ l 2 -norm to express gradual velocity tendencies in the horizontal direction, which has been often used in conventional tomography methods. We use a synthetic data set to demonstrate that our method provides significant improvements over velocity structures estimated using conventional methods by obtaining stable estimates of both steep and gradual changes in velocity. We also demonstrate that our proposed method is robust to variations in the amplitude of the velocity jump, the initial velocity model, and the number of observed arrival times, compared with conventional approaches, and verify the adaptability of the proposed method to dipping discontinuities. Furthermore, we apply our proposed method to real seismic data in central Japan and present the potential of our method for detecting velocity discontinuities using the observed arrival times from a small number of local earthquakes. Graphical Abstract
    Type of Medium: Online Resource
    ISSN: 1880-5981
    URL: Article
    DOI: 10.1186/s40623-022-01600-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2087663-4
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