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[1] Abe, H. and Yadohisa, H. (2016): Automatic Relevance Determination in Nonnegative Matrix Factorization based on Zero-inflated Compound Poisson-gamma distribution, Journal of the Japanese Society of Computational Statistics. (in press)
[2] Abe, H. and Yadohisa, H. (2016): A non-negative matrix factorization model based on the zero-inflated Tweedie distribution, Computational Statistics.(in press)
[3] Tsuchida, J. and Yadohisa, H. (2016): Asymmetric multidimensional scaling of n-mode m-way categorical data using a log-linear model, Behaviormetrika, 43, pp.103-138.
[4] Tsuchida, J., Yadohisa, H. (2016): Connected Categorical Canonical Covariance Analysis for Three-mode Three-way data Sets Based on Tucker Model, Procedia Computer Science, 96, p.912-919.
[5] Abe, H. and Yadohisa, H. (2016): Orthogonal Non-negative Matrix Tri-factorization Based on the Tweedie Family, 9th International Conference of the European Research Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics 2016, p219, University of Seville, Spain.
[6] Takagishi, M. and Yadohisa, H. (2016): Registration method for functional data based on shape invariant model with t distribution, The 22nd International Conference on Computational Statistics, p.46, Oviedo,Spain .
[7] Tsuchida, J., Yadohisa, H. (2016): Canonical covariance analysis for three-mode three-way data by using connector matrix, The 22nd International Conference on Computational Statistics, p. 3, Oviedo, Spain.
[8] Tsuchida, J., Yadohisa, H. (2016): Majorization algorithm for dominance point model, 5th German-Japanese Workshop on Classification, p.16, Gunzburg, Germany.
[9] Abe, H. and Yadohisa, H. (2016): Orthogonal Non-negative Matrix Tri-factorization Based on the Tweedie Family, The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, Hong Kong, China.
[10] Tsuchida, J. and Yadohisa, H. (2016): L1 Penalized Three-mode Three-way Canonical Covariance Analysis Based on Tucker2 Model, The 7th International Forum on Statistics of Renmin University of China, Beijing, China.
[11] Takagishi, M. and Yadohisa, H. (2016): Iteratively Reweighted Alignment Method Based on Shape Invariant Model, The 7th International Forum on Statistics of Renmin University of China, Beijing, China.
[12] Abe, H. and Yadohisa, H. (2016): Two Soft Clustering Approaches for Weighted Spherical K-means, The 7th International Forum on Statistics of Renmin University of China, Beijing, China.
[13] Tsuchida, J., Yadohisa, H. (2016): Constrained canonical covariance analysis by using Tucker2 model. Joint Statistical Meeting 2016,Chicago, U.S.A.
[14] 高岸茉莉子, 宿久洋 (2016): Shape Invariant Modelに基づく繰り返し加重アライメント法の提案, 第9回日本統計学会春季集会, (於 東北大学)．
[15] 阿部寛康, 宿久洋 (2016): Tweedie分布族に基づく非負値行列の直交制約付Tri-factorizationについて, 第9回日本統計学会春季集会, (於 東北大学)．
[16] 土田潤，宿久洋 (2016): 制約付き2相3元Dominance点モデルについて, 日本計算機統計学会第30回シンポジウム, pp.97-100, (於 プラサ ヴェルデ).
[17] 阿部博康，宿久洋 (2016):順序制約を伴う直交制約付非負値行列因子分解， 日本計算機統計学会第30回シンポジウム, pp.93-96, (於 プラサ ヴェルデ).
[18] 土田潤, 宿久洋 (2016): Dominance 点モデルのMajorization アルゴリズムについて, 日本行動計量学会第44回大会, p.90-91, (於 札幌学院大学).
[19] 阿部寛康, 宿久洋 (2016): 直交制約付き非負値テンソル因子分解について, 日本計算機統計学会第30回大会, p141-142, (於 ハートピア京都).
[20] 高木育史, 宿久洋 (2016): クラスタリングを伴う射影追跡法の提案, 日本計算機統計学会第30回大会, p157-158, (於 ハートピア京都).
[21] 高岸茉莉子, 宿久洋 (2016): t分布を用いたロバストなアライメント法の提案, 日本計算機統計学会第30回大会, p93-96, (於 ハートピア京都)．
[22] 阿部寛康, 宿久洋 (2016): 複合ポアソン分布に基づく直交制約付の非負値行列因子分解について, 「行列分解に基づく大規模複雑データ解析法に関する研究」研究会, (於 北海道大学).
[23] 谷岡健資, 宿久洋 (2016): CDSに基づく制約付き非対称多次元尺度構成法について, 「行列分解型多変量データ解析法に関する研究」研究会, (於 統計数理研究所).
[24] 阿部寛康, 宿久洋 (2016): 複合ポアソン分布に基づく非負値行列の直交制約付Tri-factorizationについて, 「行列分解型多変量データ解析法に関する研究」研究会, (於 統計数理研究所).
[25] 土田潤, 岡部格明, 宿久洋 (2016): 統計力の分類-統計検定受験者の解答を用いて-, 日本分類学会第34回大会, p34-36, (於 東海大学)
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