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A novel approach for clustering proteomics data using bayesian fast fourier transform
 Bioinformatics
, 2005
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Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation
 J Amer. Stat. Ass
, 1998
"... this paper we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also m ..."
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this paper we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also made useful for window span selection. The usefulness of the weighted version of these criteria are demonstrated through a simulation study and an application to three data sets. KEY WORDS: Information Criteria; Posterior Probability Criteria; Model Selection; Local Likelihood. 1. INTRODUCTION Local regression has become a popular method for smoothing scatterplots and for nonparametric regression in general. It has proven to be a useful tool in finding structure in datasets (Cleveland and Devlin 1988). Local regression estimation is a method for smoothing scatterplots (x i ; y i ), i = 1; : : : ; n in which the fitted value at x 0 is the value of a polynomial fit to the data using weighted least squares where the weight given to (x i ; y i ) is related to the distance between x i and x 0 . Stone (1977) shows that estimates obtained using the local regression methods have desirable theoretical properties. Recently, Fan (1993) has studied minimax properties of local linear regression. Tibshirani and Hastie (1987) extend the ideas of local regression to a local likelihood procedure. This procedure is designed for nonparametric regression modeling in situations where weighted least squares is inappropriate as an estimation method, for example binary data. Local regression may be viewed as a special case of local likelihood estimation. Tibshirani and Hastie (1987), Staniswalis (1989), and Loader (1999) apply local likelihood estimation to several types of data where local regressio...
THE TANGLED WEB OF COMMUNITY ECOLOGY: MAKING SENSE OF COMPLEX DATA
, 2006
"... tangled web of community ecology: making sense of complex data. ” I have examined ..."
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tangled web of community ecology: making sense of complex data. ” I have examined
A Diagnostic of Influential Cases Based on the Information Complexity Criteria in Mixed Models
"... Abstract: In the mixed modeling framework, we develop a diagnostic for detecting influential cases based on the information complexity criteria. The diagnostic compares the information complexity criteria between the full data set and a casedeleted data set. The computational formula of the info ..."
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Abstract: In the mixed modeling framework, we develop a diagnostic for detecting influential cases based on the information complexity criteria. The diagnostic compares the information complexity criteria between the full data set and a casedeleted data set. The computational formula of the information complexity criterion is derived using the Fisher information matrix.
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"... This paper derives a consistent and bias corrected extension of Akaike’s Information Criterion (AIC), AICbc, based on KullbackLeibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of th ..."
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This paper derives a consistent and bias corrected extension of Akaike’s Information Criterion (AIC), AICbc, based on KullbackLeibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of the asymptotically efficient model selection criteria for small and large samples will be overcome. The AICbc also provides a consistent model order selection. Thus, it is widely applicable to data with small and/or large sample sizes, and to cases where the number of free parameters is a relatively large fraction of the sample size. Relationships with other model selection criteria such as AICc of Hurvich, CAICF of Bozdogan and etc. are discussed. Empirical performances of the AICbc are studied and discussed in better model order choices of a linear regression model using a Monte Carlo experiment.
Research Article A Novel Hybrid Dimension Reduction Technique for Undersized High Dimensional Gene Expression Data Sets Using Information Complexity Criterion for Cancer Classification
"... Copyright © 2015 Esra Pamukçu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene expression data typically are large, compl ..."
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Copyright © 2015 Esra Pamukçu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features) but with only a limited number of observations (i.e., samples). Although the classical principal component analysis (PCA) method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA) by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs) to be retained, we further introduce and develop celebrated Akaike’s information criterion (AIC), consistent Akaike’s information criterion (CAIC), and the information theoreticmeasure of complexity (ICOMP) criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions. 1.
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"... This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more ..."
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This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more