Fumiyasu Komaki

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We investigate shrinkage priors for constructing Bayesian predictive distributions. It is shown that there exist shrinkage predictive distributions asymptotically dominating Bayesian predictive distributions based on the Jeffreys prior or other vague priors if the model manifold satisfies some differential geometric conditions. Kullback– Leibler divergence(More)
This paper presents kernel regularization information criterion (KRIC), which is a new criterion for tuning regularization parameters in kernel logistic regression (KLR) and support vector machines (SVMs). The main idea of the KRIC is based on the regularization information criterion (RIC). We derive an eigenvalue equation to calculate the KRIC and solve(More)
The METR technical reports are published as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this(More)
The generalized empirical likelihood (GEL) method produces a class of estimators of parameters defined via general estimating equations. This class includes several important estimators, such as empirical likelihood (EL), exponential tilting (ET), and continuous updating estimators (CUE). We examine the information geometric structure of GEL estimators. We(More)
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(More)
We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the(More)
Neural decoding is a framework for reconstructing external stimuli from spike trains recorded in brains. Kloosterman et al. (2014) proposed a new decoding method using marked point processes. This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate(More)
Bayesian testing of a point null hypothesis is considered. The null hypothesis is that an observation, x, is distributed according to the normal distribution with a mean of zero and known variance σ. The alternative hypothesis is that x is distributed according to a normal distribution with an unknown nonzero mean, μ, and variance σ. The testing problem is(More)