Results 1  10
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37
Missing value estimation methods for DNA microarrays
, 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and Kmeans clu ..."
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Cited by 477 (24 self)
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Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and Kmeans clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.
Multiway Analysis in the Food Industry  Models, Algorithms, and Applications
 MRI, EPG and EMA,” Proc ICSLP 2000
, 1998
"... This thesis describes some of the recent developments in multiway analysis in the field of chemometrics. Originally, the primary purpose of this work was to test the adequacy of multiway models in areas related to the food industry. However, during the course of this work, it became obvious that b ..."
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Cited by 117 (2 self)
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This thesis describes some of the recent developments in multiway analysis in the field of chemometrics. Originally, the primary purpose of this work was to test the adequacy of multiway models in areas related to the food industry. However, during the course of this work, it became obvious that basic research is still called for. Hence, a fair part of the thesis describes methodological developments related to multiway analysis.
Prediction of missing values in microarray and use of mixed models to evaluate the predictors
 STAT. APPL. GENET. MOL. BIOL
, 2005
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EMPIRICAL LIKELIHOOD FOR ESTIMATING EQUATIONS WITH MISSING VALUES
, 903
"... We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wideranging, we propose a nonparametric imputation of the missing values from ..."
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Cited by 7 (2 self)
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We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wideranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is further evaluated by simulation and an empirical study on a genetic dataset on recombinant inbred mice. 1. Introduction. Missing
Backfitting in Smoothing Spline ANOVA
 The Annals of Statistics
, 1998
"... A computational scheme for fitting smoothing spline ANOVA models to large data sets with a (near) tensor product design is proposed. Such data sets are common in spatialtemporal analyses. The proposed scheme uses the backfitting algorithm to take advantage of the tensor product design to save both ..."
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Cited by 6 (0 self)
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A computational scheme for fitting smoothing spline ANOVA models to large data sets with a (near) tensor product design is proposed. Such data sets are common in spatialtemporal analyses. The proposed scheme uses the backfitting algorithm to take advantage of the tensor product design to save both computational memory and time. Several ways to further speed up the backfitting algorithm, such as collapsing component functions and successive overrelaxation, are discussed. An iterative imputation procedure is used to handle the cases of near tensor product designs. An application to a global historical surface air temperature data set, which motivated this work, is used to illustrate the scheme proposed. 1. Introduction. Smoothing spline ANOVA (SSANOVA
Backfitting in smoothing spline ANOVA, with application to historical global temperature data
, 1996
"... In the attempt to estimate the temperature history of the earth using the surface observations, various biases can exist. An important source of bias is the incompleteness of sampling over both time and space. There have been a few methods proposed to deal with this problem. Although they can correc ..."
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Cited by 3 (2 self)
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In the attempt to estimate the temperature history of the earth using the surface observations, various biases can exist. An important source of bias is the incompleteness of sampling over both time and space. There have been a few methods proposed to deal with this problem. Although they can correct some biases resulting from incomplete sampling, they have ignored some other significant biases. In this dissertation, a smoothing spline ANOVA approach which is a multivariate function estimation method is proposed to deal simultaneously with various biases resulting from incomplete sampling. Besides that, an advantage of this method is that we can get various components of the estimated temperature history with a limited amount of information stored. This method can also be used for detecting erroneous observations in the data base. The method is illustrated through an example of modeling winter surface air temperature as a function of year and location. Extension to more complicated mod...
Linear Models: Least Squares and Alternatives, Second Edition
, 1995
"... The book is based on several years of experience of both authors in teaching linear models at various levels. It gives an uptodate account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for ..."
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The book is based on several years of experience of both authors in teaching linear models at various levels. It gives an uptodate account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results about the definiteness of matrices, especially for the differences of matrices, which enable superiority comparisons of two biased estimates to be made for the first time. We have attempted to provide a unified theory of inference from linear models with minimal assumptions. Besides the usual leastsquares theory,
Role of Adenosine A2 Receptors in Brain Stimulation Reward under Baseline Conditions and during Cocaine Withdrawal in Rats
"... The present experiments tested the hypothesis that adenosine A2 receptors are involved in central reward function. Adenosine receptor agonists or antagonists were administered to animals that had been trained to selfstimulate in a ratefree brain stimulation reward (BSR) task that provides current ..."
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The present experiments tested the hypothesis that adenosine A2 receptors are involved in central reward function. Adenosine receptor agonists or antagonists were administered to animals that had been trained to selfstimulate in a ratefree brain stimulation reward (BSR) task that provides current thresholds as a measure of reward. The adenosine A 2A receptorselective agonists 2p(2carboxyethyl)phenethylamino5�Nethylcarboxamido adenosine hydrochloride (CGS 21680) (0.1–1.0 mg/kg) and 2[(2aminoethylamino)carbonylethyl phenylethylamino]5�Nethylcarboxamido adenosine (APEC) (0.003–0.03 mg/kg) elevated reward thresholds without increasing response latencies, a measure of performance. Specifically, CGS 21680 had no effect on response latency, whereas APEC shortened latencies. Bilateral infusion of CGS 21680 (3, 10, and 30 ng/side), directly into the nucleus accumbens, elevated thresholds but shortened latencies.
Nonparametric Imputation of Missing Values for Estimating Equation Based Inference1
"... We propose a nonparametric imputation procedure for data with missing values and establish an empirical likelihood inference for parameters dened by general estimating equations. The imputation is carried out multiple times via a nonparametric estimator of the conditional distribution of the missing ..."
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We propose a nonparametric imputation procedure for data with missing values and establish an empirical likelihood inference for parameters dened by general estimating equations. The imputation is carried out multiple times via a nonparametric estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a prole likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more ecient parameter estimation. The proposed method is evaluated by simulation and an empirical study on the relationship between eye weight and gene transcriptional abundance of recombinant inbred mice.
ISBA President
, 2002
"... In this issue of the ISBA Bulletin I have one procedural item and one research item to share/discuss with you. On ISBA procedure, several months ago I initiated a discussion, largely by email, among the members of the ISBA Board and Executive about the desirability of beginning (in the near future) ..."
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In this issue of the ISBA Bulletin I have one procedural item and one research item to share/discuss with you. On ISBA procedure, several months ago I initiated a discussion, largely by email, among the members of the ISBA Board and Executive about the desirability of beginning (in the near future) the process by which ISBA Fellows will be elected, and I also asked for comments on this issue from the general membership. Support for the idea that we should begin this process soon, rather than at some point in the future when ISBA has been around longer (we’re 10 years old now) and/or when ISBA has more members (we have about 500 now), was strong enough that I’d like to encourage