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Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
 Proc. 1st IEEE Computer Society Bioinformatics Conference
, 2002
"... We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric ..."
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Cited by 41 (18 self)
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We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes. 1.
Statistics and Music: Fitting a Local Harmonic Model to Musical Sound Signals
, 1998
"... Statistical modeling and analysis have been applied to different music related fields. One of them is sound synthesis and analysis. Sound can be represented as a realvalued function of time. This function can be sampled at a small enough rate so that the resulting discrete version is almost as goo ..."
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Cited by 8 (4 self)
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Statistical modeling and analysis have been applied to different music related fields. One of them is sound synthesis and analysis. Sound can be represented as a realvalued function of time. This function can be sampled at a small enough rate so that the resulting discrete version is almost as good as the continuous one. This permits one to study musical sounds as a discrete time series, an entity for whichmany statistical techniques are available. Physical modeling suggests that manymusical instruments' sounds are characterized bya harmonic and an additive noise signal. The noise is not something to get rid of rather it's an important part of the signal. In this research the interest is in separating these two elements of the sound. To do so a local harmonic model that tracks ch...
Model selection in nonnested hidden Markov models
 Journal of theoretical biology
, 2001
"... An important task in the application of Markov models to the analysis of ion channel data is the determination of the correct gating scheme of the ion channel under investigation. Some prior knowledge from other experiments often allows to reduce the number of possible models signi"cantly. If t ..."
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Cited by 2 (0 self)
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An important task in the application of Markov models to the analysis of ion channel data is the determination of the correct gating scheme of the ion channel under investigation. Some prior knowledge from other experiments often allows to reduce the number of possible models signi"cantly. If these models are nested, standard statistical procedures, like likelihood ratio testing, provide reliable selection methods. In the case of nonnested models information criteria like AIC, BIC, etc., are used. However, it is not known if any of these criteria provide a reliable selection method and which is the best one in the context of ion channel gating. We provide an alternative approach to model selection in the case of nonnested models with an equal number of open and closed states. The models to choose from are embedded in a properly de"ned general model. Therefore, we circumvent the problems of model selection in the nonnested case and can apply model selection procedures for nested models. � 2001 Academic Press 1.
Estimating Term Structure Using Nonlinear Splines: A Penalized Likelihood Approach
"... The splinebased models are widely used in practice to estimate the term structure of interest rates from a set of observed couponbond prices. The most popular method can be traced back to McCulloch (1971). Assuming that the price of a bond is equal to the present value of its future coupon payment ..."
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The splinebased models are widely used in practice to estimate the term structure of interest rates from a set of observed couponbond prices. The most popular method can be traced back to McCulloch (1971). Assuming that the price of a bond is equal to the present value of its future coupon payments and redemption, cash flows are regressed on a set of basis functions to estimate discount functions. Once the discount function is estimated, the zerocoupon yield and the forward rate can be obtained by transformations of the discount function. Though this method was followed by a lot of researchers, some serious drawbacks have been reported. The most important problem is the instability of estimated yield curves. As is widely known,
FUNCTIONAL DISCRIMINANT ANALYSIS FOR MICROARRAY GENE EXPRESSION DATA VIA RADIAL BASIS FUNCTION NETWORKS
"... Abstract: We introduce functional logistic discriminant analysis (FLDA) which is an extension of the classical method of logistic discriminant analysis to data where predictor variables are functions or curves. FLDA approach can effectively classify functions into two distinct classes by imposing sm ..."
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Abstract: We introduce functional logistic discriminant analysis (FLDA) which is an extension of the classical method of logistic discriminant analysis to data where predictor variables are functions or curves. FLDA approach can effectively classify functions into two distinct classes by imposing smoothness constraint on the predictor functions and coefficient function by radial basis function expansion and regularization. In order to select the value of a smoothing parameter, we derive an information criterion which enables us to evaluate model estimated by regularization. The proposed method is illustrated through the analysis of yeast cell cycle microarray data. It is shown that FLDA performs well especially in terms of prediction ability. 1
Statistical Model Evaluation by Generalized Information Criteria  Bias and Variance Reduction Techniques 
"... The problem of evaluating the goodness of statistical models is crucially important in various elds of statistical science. Akaike's (1973) information criterion provides a useful tool for evaluating models estimated by the method of maximum likelihood. In recent years advances in the performance of ..."
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The problem of evaluating the goodness of statistical models is crucially important in various elds of statistical science. Akaike's (1973) information criterion provides a useful tool for evaluating models estimated by the method of maximum likelihood. In recent years advances in the performance of computers enables us to construct complicated models for
Simultaneous Term Structure Estimation of Hazard and Loss Given Default with a Statistical Model using Credit Rating and Financial
"... Information ..."
Journal of Statistical Planning and
, 2000
"... www.elsevier.com/locate/jspi Improving predictive inference under covariate shift by weighting the loglikelihood function ..."
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www.elsevier.com/locate/jspi Improving predictive inference under covariate shift by weighting the loglikelihood function
Devant le jury:
"... pour obtenir le grade de Docteur de l’Institut des Sciences et Industries du Vivant et de l’Environnement (Agro Paris Tech) Spécialité: STATISTIQUE Présentée et soutenue publiquement ..."
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pour obtenir le grade de Docteur de l’Institut des Sciences et Industries du Vivant et de l’Environnement (Agro Paris Tech) Spécialité: STATISTIQUE Présentée et soutenue publiquement
Kyoto University
"... Since the invention of temporal difference (TD) learning (Sutton, 1988), many new algorithms for modelfree policy evaluation have been proposed. Although they have brought much progress in practical applications of reinforcement learning (RL), there still remain fundamental problems concerning stat ..."
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Since the invention of temporal difference (TD) learning (Sutton, 1988), many new algorithms for modelfree policy evaluation have been proposed. Although they have brought much progress in practical applications of reinforcement learning (RL), there still remain fundamental problems concerning statistical properties of the value function estimation. To solve these problems, we introduce a new framework, semiparametric statistical inference, to modelfree policy evaluation. This framework generalizes TD learning and its extensions, and allows us to investigate statistical properties of both of batch and online learning procedures for the value function estimation in a unified way in terms of estimating functions. Furthermore, based on this framework, we derive an optimal estimating function with the minimum asymptotic variance and propose batch and online learning algorithms which achieve the optimality.