bluuebunny/crossref_metadata_2025 huggingface dataset:
Do Economists Recognize an Opportunity Cost When They See One? A Dismal Performance from the Dismal Science
Authors: Ferraro Paul J , Taylor Laura O
Published: December 2005

One expects people with graduate training in economics to have a deeper understanding of economic processes and reasoning than people without such training. However, as others have noted over the past 25 years, modern graduate education may emphasize mathematics and technique to the detriment of economic reasoning. One of the most important contributions economics has to offer as a discipline is the understanding of opportunity cost and how to apply this concept to all forms of decision making. We examine how PhD economists answer an introductory economics textbook question that requires identifying the relevant opportunity cost of an action. The results are not consistent with our expectation that graduate training leads to a deeper understanding of the concept. We explore the implications of our results for the relevance of economists in policy, research, and teaching.
Using the Survivor Technique to Estimate Returns to Scale and Optimum Firm Size
Authors: Giordano James N
Published: December 2005

The survivor technique for estimating returns to scale and optimum firm size has generated a slow but steady literature since its 1958 pilot presentation by George Stigler. This article (1) integrates advances in its application into a complete demonstration of how the technique works, (2) distinguishes a survivor analysis from the related but different analyses of individual firm growth and size distribution as addressed, for example, by Gibrat's Law of Proportionate Effect, (3) surveys a few exemplary survivor analyses, highlighting their alternative measures of scale and survival, and (4) unifies the scattered discussion of criticisms and qualifications that surround the technique. Accordingly, this essay seeks to reposition the survivor technique as a viable statistical option for research on those industries which meet its criteria.
Mergers and Deterrence
Authors: Richards Daniel J
Published: December 2005

Changes in technology or policy create opportunities for industry restructuring and are frequently accompanied by both numerous mergers and potential entry. In the case of mergers, the combining firms have inside information regarding the strength of the surviving entity. In turn, such strength may be signaled to potential rivals or entrants by means of the acquisition price paid for the target firm. A pooling equilibrium is then possible in which even weak mergers are associated with a high acquisition price meant to deter post-merger rival aggression. The implications of such strategic behavior are consistent with much empirical evidence on mergers.
Commitment and Discretion in Health Care Policy Making
Authors: Rosenman Robert , Friesner Daniel
Published: December 2005

This paper analyzes the welfare effect from government sponsored insurance under two policy regimes for reimbursement levels – the first under which policy makers commit to a specific reimbursement schedule, the second which allows discretion in setting reimbursement schedules. We find that prices and quality will differ depending on which policy regimen is followed. We are able to identify a level for the policy maker’s elasticity of utility with respect to its policy tool under which government insured patients are unambiguously better off with policy commitment. The ordering of welfare is reversed if the elasticity of utility is reversed. In either case, privately insured patients may be better or worse off.
Information Transmission and the Bounds to Growth
Authors: Cozzi Guido , Spinesi Luca
Published: December 2005

This paper studies the long run growth implications of the presence of information acquisition and transmission costs. We assume that vertical innovation requires researchers to be informed on the current version of the product they want to improve upon; and we also assume that quasi-fixed managerial inputs are required for production in the manufacturing sector. Despite the fact the increases in total factor productivity cause R&D and managerial quasi-fixed labor costs to decrease in the same way as variable labor costs, the presence of these costs is sufficient to rule out the strong scale effect at all levels of the intertemporal returns to ideas. More importantly, the upper bound of long run growth rates crucially depends on information transmission costs.
Can Price Discrimination be Bad for Firms and Good for All Consumers? A Theoretical Analysis of Cross-Market Price Constraints with Entry and Product Differentiation
Authors: Azar Ofer H
Published: December 2005

The article examines a differentiated-products duopoly model where the firms make entry decisions to two markets and then choose prices. The effects of product differentiation and entry costs are analyzed in two games: with and without price discrimination between the markets. Allowing price discrimination encourages more entry and tends to reduce prices and profits and to increase consumer welfare in both markets. The model suggests that firms might be better off if they agree not to price discriminate between different markets. It also suggests that when the market is not a natural monopoly, regulators should consider the effects of universal service requirements on entry before adopting them, because entry might be discouraged by such requirements, leading to less competitive markets.
Membership Diversity in Club Decisions
Authors: Biglaiser Gary , Friedman James
Published: December 2005

We examine a club model in which the club's policies are controlled by a homogeneous group of insiders. Their policy decision is to determine the number they will admit from two outsider groups. Countries have immigration policies, private clubs have membership policies, and professional practices also have membership criteria in which individuals may be screened. In all these organizations new members bring a material benefit to the organization, but do so at the cost of introducing a socially differentiated population of outsiders that the majority typically dislikes. It is the interplay of the material benefit and the social cost on which we focus. We characterize steady state equilibria and examine comparative statics results.
A Note on Weak Double Dividends
Authors: Metcalf Gilbert E. , Babiker Mustafa H. , Reilly John
Published: December 2005

A weak double-dividend is the proposition that the welfare improvement from a green tax reform, where the revenue from an environmental tax is used to reduce other tax rates, must be greater than the welfare improvement from a reform where the environmental taxes are returned in a lump sum fashion. We show in this note that a weak double-dividend need not hold in a world with multiple distortions. In an economy with multiple distortions one must choose carefully which tax rates to reduce, or one can do worse than a lump sum redistribution of the environmental tax revenues.
Citizens Should Vote on Secession
Authors: Eerola Essi , Määttänen Niku , Poutvaara Panu
Published: December 2005

The draft treaty establishing a constitution for the European Union states that each member state may withdraw from the European Union following its own constitutional requirements. We argue that such a rule could lead into an increased use of threat of withdrawal to extract concessions in intergovernmental negotiations. This problem would be exacerbated by national electorates facing an incentive to elect more confrontational politicians. We also suggest a remedy: EU constitution should require that withdrawal from EU membership must be approved by the voters of the withdrawing member state in a referendum.
Some Unintended Consequences of TRQ Liberalization
Authors: Gervais Jean-Philippe , Rude James I
Published: December 2005

This paper analyzes the impacts of trade liberalization in a non-integrated industry where a marketing agency controls the supply of a necessary input in the production of a processed commodity. Trade of the processed good is restricted through a Tariff-Rate Quota (TRQ) and domestic processors operate in an imperfectly competitive industry. Under a fixed proportions technology, an increase in the minimum access commitment of the TRQ will generally lower the domestic retail price to consumers if the marketing agency is a profit-maximizing monopolist. Conversely, if the marketing agency pursues some specific non-economic objectives, it is shown that increases in the minimum access commitment holding the tariff rates constant can lead to higher retail prices and thus negate the benefits of trade liberalization for consumers.
An Essay on Cooperative Bargaining in U.S. Agricultural Markets
Authors: Hueth Brent , Marcoul Philippe
Published: December 2005

We study the incidence and economic rationale for cooperative bargaining in U.S. agricultural markets. Bargaining is not just about increasing price paid to farmers; indeed, there is no empirical research indicating that cooperative bargaining has any direct influence on price. Nevertheless, the price negotiation process may be useful in itself as a form of price discovery in markets where there is uncertainty about market supply and demand conditions, and bargaining associations can play an important role in ensuring contract reliability. These and other benefits must be weighed against the organizational and on-going operational costs of a formal bargaining association when considering whether bargaining is appropriate for a given market environment. Even when the aggregate net benefits associated with bargaining are positive, the distribution of benefits across the various market participants may have an important influence on the political feasibility of bargaining
Explaining Price Dispersion for Homogeneous Grocery Products
Authors: Aalto-Setälä Ville
Published: December 2005

This study examines the dispersion in prices for homogeneous grocery products, and studies the effect of product characteristics, demographic characteristics of consumers, and market structure on search costs and price dispersion. The findings show that price dispersion is affected by search costs. The variables that increased relative price dispersion the most were low budget share and low price of the product. Demographic characteristics affected price dispersion as well: a high proportion of families with children and a low medium income in the market area decreased dispersion in prices. Market structure did not affect price dispersion in the study data.
Prediction of the Gas Holdup in Industrial-Scale Bubble Columns and Slurry Bubble Column Reactors Using Back-Propagation Neural Networks
Authors: Behkish Arsam , Lemoine Romain , Sehabiague Laurent , Oukaci Rachid , Morsi Badie I
Published: December 2005

The total gas holdup and the holdup of large gas bubbles were predicted in bubble column reactors (BCRs) and slurry bubble column rectors (SBCRs) using two Back-Propagation Neural Networks (BPNNs). Over 3880 and 1425 data points for gas holdup and Large gas bubble holdup respectively, covering wide ranges of gas-liquid-solid physical properties, operating variables, reactor geometry, and gas sparger type/size, were employed to develop, train and validate the two neural networks. The developed BPNN for gas holdup has a topology of [14,9-7,1] and was able to predict the trained and untrained data with an average absolute relative error (AARE), standard deviation, and regression coefficient (R2) of 16, 19 and 90%, respectively. The developed BPNN for large gas bubble holdup has a topology of [14,8,1] and was capable of predicting the trained and untrained data with AARE, standard deviation, and R2 of 10, 14 and 93%, respectively. The BPNNs were then used to predict the effects of pressure, superficial gas velocity, temperature and catalyst loading on the total syngas holdup for Low-Temperature Fischer-Tropsch (LTFT) synthesis carried out in a 5 m ID SBCR. The predicted total syngas holdup appeared to increase with increasing reactor pressure, superficial gas velocity and the number of orifices in the gas sparger. The predicted syngas holdup, however, was found to decrease with increasing catalyst loading and reactor temperature. Also, under similar LTFT operating conditions (P = 3 MPa, T = 513 K, CW = 30 and 50 wt%), the total syngas holdup values predicted for H2/CO ratio of 2:1 and cobalt-based catalyst are consistently lower than those obtained for H2/CO ratio of 1:1 and iron oxide catalyst in the superficial gas velocity range from 0.005 to 0.4 m/s. These predictions are in perfect agreement with reported literature trends, which underscore the reliability and validity of the developed BPNNs in predicting the total syngas holdup and the holdup of large gas bubbles in large-scale bubble columns and SBCRs operating under industrial conditions.
Modelling a Biochemical Reaction with Computational Fluid Dynamics
Authors: Generalis Sotos C. , Cartland Glover Gregory M
Published: December 2005

Earlier investigations (Cartland Glover et al., 2004) into the use of computational fluid dynamics (CFD) for the modelling of gas-liquid and gas-liquid-solid flow allowed a simple biochemical reaction model to be implemented. A single plane mesh was used to represent the transport and reaction of molasses, the mould Aspergillus niger and citric acid in a bubble column with a height to diameter aspect ratio of 20:1. Two specific growth rates were used to examine the impact that biomass growth had on the local solids concentration and the effect this had on the local hydrodynamics of the bubble column.
Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
Authors: Vitek Olga , Vitek Jan , Craig Bruce , Bailey-Kellogg Chris
Published: December 2005

Nuclear Magnetic Resonance (NMR) spectroscopy is a key experimental technique used to study protein structure, dynamics, and interactions. NMR methods face the bottleneck of spectral analysis, in particular determining the resonance assignments, which help define the mapping between atoms in the protein and peaks in the spectra. A substantial amount of noise in spectral data, along with ambiguities in interpretation, make this analysis a daunting task, and there exists no generally accepted measure of uncertainty associated with the resulting solutions. This paper develops a model-based inference approach that addresses the problem of characterizing uncertainty in backbone resonance assignment. We argue that NMR spectra are subject to random variation, and ignoring this stochasticity can lead to false optimism and erroneous conclusions. We propose a Bayesian statistical model that accounts for various sources of uncertainty and provides an automatable framework for inference. While assignment has previously been viewed as a deterministic optimization problem, we demonstrate the importance of considering all solutions consistent with the data, and develop an algorithm to search this space within our statistical framework. Our approach is able to characterize the uncertainty associated with backbone resonance assignment in several ways: 1) it quantifies of uncertainty in the individually assigned resonances in terms of their posterior standard deviations; 2) it assesses the information content in the data with a posterior distribution of plausible assignments; and 3) it provides a measure of the overall plausibility of assignments. We demonstrate the value of our approach in a study of experimental data from two proteins, Human Ubiquitin and Cold-shock protein A from E. coli. In addition, we provide simulations showing the impact of experimental conditions on uncertainty in the assignments.
Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates
Authors: Dudoit Sandrine , van der Laan Mark J. , Pollard Katherine S.
Published: December 2005

The present article proposes general single-step multiple testing procedures for controlling Type I error rates defined as arbitrary parameters of the distribution of the number of Type I errors, such as the generalized family-wise error rate. A key feature of our approach is the test statistics null distribution (rather than data generating null distribution) used to derive cut-offs (i.e., rejection regions) for these test statistics and the resulting adjusted p-values. For general null hypotheses, corresponding to submodels for the data generating distribution, we identify an asymptotic domination condition for a null distribution under which single-step common-quantile and common-cut-off procedures asymptotically control the Type I error rate, for arbitrary data generating distributions, without the need for conditions such as subset pivotality. Inspired by this general characterization of a null distribution, we then propose as an explicit null distribution the asymptotic distribution of the vector of null value shifted and scaled test statistics. In the special case of family-wise error rate (FWER) control, our method yields the single-step minP and maxT procedures, based on minima of unadjusted p-values and maxima of test statistics, respectively, with the important distinction in the choice of null distribution. Single-step procedures based on consistent estimators of the null distribution are shown to also provide asymptotic control of the Type I error rate. A general bootstrap algorithm is supplied to conveniently obtain consistent estimators of the null distribution. The special cases of t- and F-statistics are discussed in detail. The companion articles focus on step-down multiple testing procedures for control of the FWER (van der Laan et al., 2004b) and on augmentations of FWER-controlling methods to control error rates such as tail probabilities for the number of false positives and for the proportion of false positives among the rejected hypotheses (van der Laan et al., 2004a). The proposed bootstrap multiple testing procedures are evaluated by a simulation study and applied to genomic data in the fourth article of the series (Pollard et al., 2004).
MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data
Authors: Cope Leslie , Zhong Xiaogang , Garrett Elizabeth , Parmigiani Giovanni
Published: December 2005

Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include “integrative correlation" plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression.
Using Importance Sampling to Improve Simulation in Linkage Analysis
Authors: Ängquist Lars , Hössjer Ola
Published: December 2005

In this article we describe and discuss implementation of a weighted simulation procedure, importance sampling, in the context of nonparametric linkage analysis. The objective is to estimate genome-wide p-values, i.e. the probability that the maximal linkage score exceeds given thresholds under the null hypothesis of no linkage. In order to reduce variance of the estimate for large thresholds, we simulate linkage scores under a distribution different from the null with an artificial disease locus positioned somewhere along the genome. To compensate for the fact that we simulate under the wrong distribution, the simulated scores are reweighted using a certain likelihood ratio. If the sampling distribution are properly chosen the variance of the corresponding estimate is reduced. This results in accurate genome-wide p-value estimates for a wide range of large thresholds with a substantially smaller cost adjusted relative efficiency with respect to standard unweighted simulation.

We illustrate the performance of the method for several pedigree examples, discuss implementation including the amount of variance reduction and describe some possible generalizations.
Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
Authors: García de la Nava Jorge , van Hijum Sacha , Trelles Oswaldo
Published: December 2005

We present a mathematical model to extend the dynamic range of gene expression data measured by laser scanners. The strategy is based on the rather simple but novel idea of producing two images with different scanner sensitivities, obtaining two different sets of expression values: the first is a low-sensitivity measure to obtain high expression values which would be saturated in a high-sensitivity measure; the second, by the converse strategy, obtains additional information about the low-expression levels. Two mathematical models based on linear and gamma curves are presented for relating the two measurements to each other and producing a coherent and extended range of values. The procedure minimizes the quantization relative error and avoids the collateral effects of saturation. Since most of the current scanner devices are able to adjust the saturation level, the strategy can be considered as a universal solution, and not dependent on the image processing software used for reading the DNA chip. Various tests have been performed, on both proprietary and public domain data sets, showing a reduction of the saturation and quantization effects, not achievable by other methods, with a more complete description of gene-expression data and with a reasonable computational complexity.
Response to Foulkes and De Gruttola
Authors: Segal Mark R , Barbour Jason D , Grant Robert M
Published: December 2005

This note is the authors' response to the Reader's Reaction provided by Foulkes and De Gruttola as published in Statistical Applications in Genetics and Molecular Biology
Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
Authors: Cheng Cheng , Pounds Stanley B. , Boyett James M. , Pei Deqing , Kuo Mei-Ling , Roussel Martine F.
Published: December 2005

The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as the q-value plots, allows the investigator to examine the FDR from several perspectives. However, when FDR control at the “customary" levels 0.01, 0.05, or 0.1 does not provide fruitful findings, there is little guidance for making the trade off between the significance threshold and the FDR level by sound statistical or biological considerations. Thus, meaningful statistical significance criteria that complement the existing FDR methods for large-scale multiple tests are desirable. Three statistical significance criteria, the profile information criterion, the total error proportion, and the guide-gene driven selection, are developed in this research. The first two are general significance threshold criteria for large-scale multiple tests; the profile information criterion is related to the recent theoretical studies of the connection between FDR control and minimax estimation, and the total error proportion is closely related to the asymptotic properties of FDR control in terms of the total error risk. The guide-gene driven selection is an approach to combining statistical significance and the existing biological knowledge of the study at hand. Error properties of these criteria are investigated theoretically and by simulation. The proposed methods are illustrated and compared using an example of genomic screening for novel Arf gene targets. Operating characteristics of q-value and the proposed significance threshold criteria are investigated and compared in a simulation study that employs a model mimicking a gene regulatory pathway. A guideline for using these criteria is provided. Splus/R code is available from the corresponding author upon request.
Mammalian Genomes Ease Location of Human DNA Functional Segments but Not Their Description
Authors: Newberg Lee A , Lawrence Charles E
Published: December 2005

Under the assumption that a significant motivation for sequencing the genomes of mammals is the resulting ability to help us locate and characterize functional DNA segments shared with humans, we have developed a statistical analysis to quantify the expected advantage. Examining uncertainty in terms of the width of a confidence interval, we show that uncertainty in the rate of nucleotide mutation can be shrunk by a factor of nearly four when nine mammals; human, chimpanzee, baboon, cat, dog, cow, pig, rat, mouse; are used instead of just two; human and mouse. Contrastingly, we show confidence interval shrinkage by a factor of only 1.5 for measurements of the distribution of nucleotides at an aligned sequence site. These additional genomes should greatly help in identifying conserved DNA sites, but would be much less effective at precisely describing the expected pattern of nucleotides at those sites.
On the Dependence Structure of Sequence Alignment Scores Calculated with Multiple Scoring Matrices
Authors: Frommlet Florian , Futschik Andreas
Published: December 2005

A common practice in protein sequence alignment is to try several scoring matrices until “something interesting" is found. This leads to a multiple testing problem making p- and E-values hard to interpret. We focus on local alignment and propose to use logistic copula functions to model explicitly the dependence structure of scores obtained using different scoring matrices. By doing this, we obtain p-value correction factors when using more than one scoring matrix on the same sequences. Furthermore the parameter of the logistic copula can be interpreted as measure of dependence, providing insight concerning the relatedness of the scores from different matrices.
Confidence Levels for the Comparison of Microarray Experiments
Authors: Shedden Kerby
Published: December 2005

A common experimental strategy utilizing microarrays is to develop a signature of genes responding to some treatment in a model system, and then ask whether the same genes respond in an analogous way in a more natural and uncontrolled environment. In statistical terms, the question posed is whether genes score similarly on some statistical test in two independent data sets. Approaches to this problem ignoring gene/gene correlations common to all microarray data sets are known to give overstated statistical confidence levels. Permutation approaches have been proposed to give more accurate confidence levels, but can not be applied when sample sizes are small. Here we argue that the product moment correlation between test statistics in the two experiments is an ideal measure for summarizing concordance between the experiments, as confidence levels accounting for intergene correlations depend only on a single number -- the average squared correlation between gene pairs in the data set. The resulting null standard deviation is shown to vary by less than a factor of two over six distinct experimental data sets, suggesting that a universal constant may be used for this quantity. We show how a hidden assumption of the permutation approach may lead to incorrect p-values, while the analytic approach presented here is shown to be resistant to this assumption.
Combined Association and Linkage Analysis for General Pedigrees and Genetic Models
Authors: Hössjer Ola
Published: December 2005

A combined score test for association and linkage analysis is introduced, based on a biologically plausible model with association between markers and causal genes and penetrance between phenotypes and the causal gene. The test is based on a retrospective likelihood of marker data given phenotypes, treating the alleles of the causal gene as hidden data. It is defined for arbitrary outbred pedigrees, a wide class of genetic models including polygenic and shared environmental effects and allows for missing marker data. It is multipoint, taking marker genotypes from several loci into account simultaneously. The score vector has one association and one linkage component, which can be used to define separate tests for association and linkage. For complete marker data, we give closed form expressions for the efficiency of the linkage, association and combined tests. These are examplified for binary and quantitative phenotypes with or without polygenic effects. The conclusion is that association tests are comparatively more efficient than linkage tests for strong association, weak penetrance models, small families and non-extreme phenotypes, whereas the linkage test is more efficient for weak association, strong penetrance models, large families and extreme phenotypes. The combined test is a robust alternative, which never performs much worse than the best of the linkage and association tests, and sometimes significantly better than both of them. It should be particularly useful when little is known about the genetic model.
Stepwise Normalization of Two-Channel Spotted Microarrays
Authors: Xiao Yuanyuan , Segal Mark R , Yang Yee Hwa
Published: December 2005

Intensities measurements of spotted microarrays embody many undesirable systematic variations. Very commonly, varying amounts and types of such variations are observed in different arrays. Although various normalization methods have been proposed to remove such systematic effects, it has not been well studied how to assess or select the most appropriate method for different arrays and data sets. To address this issue, we present a novel normalization technique, STEPNORM, for data-dependent and adaptive normalization of two-channel spotted microarrays. STEPNORM performs a stepwise interrogation of a range of different normalization models and selects the appropriate method based on formal model selection criteria. In addition, we evaluate the effectiveness of STEPNORM and other commonly used normalization methods utilizing a set of specially constructed splicing arrays.
Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors
Authors: Feten Guri , Almøy Trygve , Aastveit Are H.
Published: December 2005

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.
Empirical Bayes and Resampling Based Multiple Testing Procedure Controlling Tail Probability of the Proportion of False Positives.
Authors: van der Laan Mark J. , Birkner Merrill D. , Hubbard Alan E.
Published: December 2005

Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. In this article we propose a new re-sampling based multiple testing procedure asymptotically controlling the probability that the proportion of false positives among the set of rejections exceeds q at level alpha, where q and alpha are user supplied numbers. The procedure involves 1) specifying a conditional distribution for a guessed set of true null hypotheses, given the data, which asymptotically is degenerate at the true set of null hypotheses, and 2) specifying a generally valid null distribution for the vector of test-statistics proposed in Pollard & van der Laan (2003), and generalized in our subsequent article Dudoit, van der Laan, & Pollard (2004), van der Laan, Dudoit, & Pollard (2004), and van der Laan, Dudoit, & Pollard (2004b). Ingredient 1) is established by fitting the empirical Bayes two component mixture model (Efron (2001b)) to the data to obtain an upper bound for marginal posterior probabilities of the null being true, given the data. We establish the finite sample rational behind our proposal, and prove that this new multiple testing procedure asymptotically controls the wished tail probability for the proportion of false positives under general data generating distributions. In addition, we provide simulation studies establishing that this method is generally more powerful in finite samples than our previously proposed augmentation multiple testing procedure (van der Laan, Dudoit, & Pollard (2004b)) and competing procedures from the literature. Finally, we illustrate our methodology with a data analysis.
Fold-Change Estimation of Differentially Expressed Genes using Mixture Mixed-Model
Authors: Gusnanto Arief , Ploner Alexander , Pawitan Yudi
Published: December 2005

Microarray experiments produce expression measurements for thousands of genes simultaneously, though usually for a small number of RNA samples. The most common problem is the identification of genes that are differentially expressed between different groups of samples or biological conditions. As the number of genes far exceeds the number of RNA samples, the inherent multiplicity poses a severe problem in both hypothesis testing and effect estimation. While much of the recent literature is focused on the hypothesis aspects, we concentrate in this paper on effect estimation as a tool for the identification of differentially expressed genes. We propose a linear mixed model where the random effects are assumed to follow a mixture distribution, and study in detail the case of three normals, corresponding to genes that are down-, up- or non regulated. Our approach leads to a new type of non-linear shrinkage estimation, where a proportion of estimates is shrunk to zero, while the rest follows standard linear shrinkage. This allows us to estimate the log fold-change of the genes involved and to identify those that are differentially expressed within the same model framework. We investigate the operating characteristics of our method using simulation and spike-in studies, and illustrate its application to real data using a breast-cancer dataset.
Test on the Structure of Biological Sequences via Chaos Game Representation
Authors: Cénac Peggy
Published: December 2005

In this paper biological sequences are modelled by stationary ergodic sequences. A new family of statistical tests to characterize the randomness of the inputs is proposed and analyzed. Tests for independence and for the determination of the appropriate order of a Markov chain are constructed with the Chaos Game Representation (CGR), and applied to several genomes.
Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation
Authors: Xiao Yuanyuan , Segal Mark R
Published: December 2005

Identification of peptides binding to Major Histocompatibility Complex (MHC) molecules is important for accelerating vaccine development and improving immunotherapy. Accordingly, a wide variety of prediction methods have been applied in this context. In this paper, we introduce (tree-based) ensemble classifiers for such problems and contrast their predictive performance with forefront existing methods for both MHC class I and class II molecules. In addition, we investigate the impact of differing peptide representation schemes on performance. Finally, classifier predictions are used to conduct genomewide scans of a diverse collection of HIV-1 strains, enabling assessment of epitope conservation. We investigated all combinations of six classification methods (classification trees, artificial neural networks, support vector machines, as well as the more recently devised ensemble methods (bagging, random forests, boosting) with four peptide representation schemes (amino acid sequence, select biophysical properties, select quantitative structure-activity relationship (QSAR) descriptors, and the combination of the latter two) in predicting peptide binding to an MHC class I molecule (HLA-A2) and MHC class II molecule (HLA-DR4). Our results show that the ensemble methods are consistently more accurate than the other three alternatives. Furthermore, they are robust with respect to parameter tuning. Among the four representation schemes, the amino acid sequence representation gave consistently (across classifiers) best results. This finding obviates the need for feature selection strategies incurred by use of biophysical and/or QSAR properties. We obtained, and aligned, a diverse set of 32 HIV-1 genomes and pursued genomewide HLA-DR4 epitope profiling by querying with respect to classifier predictions, as obtained under each of the four peptide representation schemes. We validated those epitopes conserved across strains against known T-cell epitopes. Once again, amino acid sequence representation was at least as effective as using properties. Assessment of novel epitope predictions awaits experimental verification.
Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models
Authors: Plötz Thomas , Fink Gernot A.
Published: December 2005

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we propose feature based semi-continuous Profile HMMs. Based on a richer sequence representation consisting of features which capture the biochemical properties of residues in their local context, family specific semi-continuous models are estimated completely data-driven. Additionally, for substantially reducing the number of false predictions an explicit rejection model is estimated. Both the family specific semi-continuous Profile HMM and the non-target model are competitively evaluated. In the experimental evaluation of superfamily based screening of the SCOP database we demonstrate that semi-continuous Profile HMMs significantly outperform their discrete counterparts. Using the rejection model the number of false positive predictions could be reduced substantially which is an important prerequisite for target identification applications.
Bayesian Statistical Studies of the Ramachandran Distribution
Authors: Pertsemlidis Alexander , Zelinka Jan , Fondon John W. , Henderson R. Keith , Otwinowski Zbyszek
Published: December 2005

We describe a method for the generation of knowledge-based potentials and apply it to the observed torsional angles of known protein structures. The potential is derived using Bayesian reasoning, and is useful as a prior for further such reasoning in the presence of additional data. The potential takes the form of a probability density function, which is described by a small number of coefficients with the number of necessary coefficients determined by tests based on statistical significance and entropy. We demonstrate the methods in deriving one such potential corresponding to two dimensions, the Ramachandran plot. In contrast to traditional histogram-based methods, the function is continuous and differentiable. These properties allow us to use the function as a force term in the energy minimization of appropriately described structures. The method can easily be extended to other observable angles and higher dimensions, or to include sequence dependence and should find applications in structure determination and validation.
The Chinese Quality Of Life Instrument: Development Of A New Health-Related Quality Of Life Instrument Using Factor Analysis And Structural Equation Modeling
Authors: Zhao Li , Chan Kelvin , Leung Kwok-fai , Liu Feng-bin , Lang Jian-ying , Fang Ji-qian
Published: December 2005

A new Chinese Quality of Life Instrument (ChQOL) based on the principles of diagnosis and practice in Chinese medicine has been developed. This paper describes the development of the ChQOL instrument using factor analysis and structural equation modeling. An initial pilot version of the 78 items instrument was field tested in a sample 273 subjects recruited from different areas in China. The objective of this study was to determine the factor structure and latent constructs of the ChQOL based on factor analysis. A series of confirmatory factor analysis and structural equation models were used to determine the final model of the ChQOL. The results showed that the application of factor analysis and structural equation modeling is an effective method to develop the new health-related quality of life instrument, ChQOL, even though the practice of Chinese medicine is quite different from the practice of conventional western medicine.
Status of Legislation of Herbal Medicinal Products in Iberoamerica
Authors: Gupta Mahabir P
Published: December 2005

The current status of registration of plant-based and natural products medicines in 15 Latinoamerican countries and Spain is presented . The requirements for registration and quality control of these products vary significantly from country to country. There is an urgent need for harmonization. Worldwide collaboration and communication processes should be encouraged and several existing programs must be supported.
From Diversity Towards Harmonisation
Authors: Cranz Hubertus
Published: December 2005

The legislation and regulatory situation of herbal medicinal products in the European Union has changed considerably over the last years. The new system provides better opportunities for citizens to all countries of the European Union to have at their availability a wider range of herbal medicines.
Clinical Observation of 11 Patients with Severe Acute Respiratory Syndrome Treated with Traditional Chinese Medicine
Authors: Tong Xiaolin , Li Aiguo , Zhang Zhiyuan , Chen Xiaoguang , Li Ping , Gong Yuewen , Duan Jun , Hua Chuanjin , Zhao Dong , Xu Yuan , Shi Xiaoping , Tian Xin , Lin Fan , Cao Yongtong , Lu Jin , Chang Ming , Wang Yunting , Li Ping
Published: December 2005

Background: During the outbreak of severe acute respiratory syndrome (SARS) in the Spring 2003, we employed the theory of traditional Chinese medicine (TCM) to treat patients with SARS in China-Japan Friendship Hospital. Methods: According to our clinical observation with the Pestilence theory of TCM, the progress of SARS can be divided into five stages, i.e. the latent stage, the febrile stage, the cough and asthmatic stage, the prostration stage, and the recovery stage. TCM formulae were employed for the different stages of SARS. The time of allaying fever, the average absorption day of pulmonary infiltration, and the increase in white blood cell counts were used for evaluation of the treatment. Results: For those 4 patients with fever at the time of admitting to hospital, the time of allaying fever was 2, 3, 6 and 7 days respectively after the treatment of TCM. For 11 patients, the mean absorption time of chest infiltration was 14.56±6.71 days from the range of 5 days to 25 days. In addition, after treatment with TCM, total count of white blood cells and lymphocytes were all within normal range. Conclusion: Treatment with traditional Chinese medicine can be one of the choices for patients with SARS.
Design of a Fuzzy Sliding Mode Controller by Genetic Algorithms for Induction Machine Speed Control
Authors: Hazzab Abdeldjebar , Bousserhane Ismail Khalil , Kamli Mokhtar
Published: December 2005

The drawbacks of sliding mode control in terms of high control gains and chattering are overcome by merging of the FLC with the variable structure of the SMC to form a fuzzy sliding mode controller (FSMC). However, the major drawback of fuzzy control is the lack of design techniques. Hence this hybrid system increases the complexity in design and, at present, there exists no effective design tools due to the lack of analytical and numerical approaches. This paper develops an automated design approach to this design problem, using a genetic algorithm. The method is illustrated through the design of a near-optimal fuzzy sliding mode controller for induction motor speed control. The control strategy gives a relatively low overshoots with smooth control action and retains robustness of the sliding mode approach.
An Optimum UPQC with Minimum VA Requirement and Mitigation of Unbalanced Voltage Sag
Authors: Kolhatkar Y. Y. , Das S. P.
Published: December 2005

The paper deals with an optimized Unified Power Quality Conditioner (UPQC), which aims at the integration of series active and shunt active power filters with minimum VA loading of the UPQC. The series active filter is a Dynamic Voltage Restorer (DVR), which is operated in twofold functioning mode. During the unbalanced voltage sag, the DVR compensates the unbalance in the voltage sag. It also regulates the voltage at the load end with minimum VA loading of the overall UPQC by optimum angle voltage injection. Validity of the proposed scheme is proved through extensive simulation results.
Implications of Carbon Tax on Generation Expansion Plan & GHG Emission: A Case Study on Indian Power Sector
Authors: Nanda Gaurav , Yamgar Sangeeta , Srivastava S.C. , Singh S.N. , Gupta Praveen , Paul Dharam , Shrestha Ram M
Published: December 2005

Concentration of greenhouse gases in the atmosphere is steadily increasing leading to global warming. India is expected to be one of the major contributors to Green House Gases (GHGs) due to increased share of thermal power generation, which is a major contributor to carbon dioxide (CO2) emission. One way to limit these emissions is by implementing some economic instruments like carbon tax or energy tax. This study has been carried out to analyze the impact of carbon tax on the complete Indian power sector network. An Integrated Resource Planning and Analysis (IRPA) model and CPLEX software has been used to carry out the present study.
Novel Method of Ranking Line Outage Contingencies from Network Loadability Limit Point of View
Authors: Bijwe P. R. , Hanmandlu M. , Pande V. N. , Kelapure S. M.
Published: December 2005

This paper presents novel, simple and efficient method for ranking line outage contingencies from network loadability limit considerations. The method follows conventional optimal multiplier based Newton Raphson power flow. The simulation of line outage contingencies is carried out near pre-contingency critical loading using the above power flow. A salient feature of the method is the use of the pre-contingency power flow Jacobian factors for simulation of all contingencies. Results for two sample test systems have been obtained with the new method and continuation power flow method in order to verify the potential of the former method for practical use.
A New Phase Advanced Multiloop Control System For Dynamic Voltage Restorer
Authors: Taghikhani Mohammad Ali , Kazemi Ahad
Published: December 2005

A dynamic voltage restorer is a power quality (custom power) device used to correct the voltage disturbances by injecting voltage as well as power into the system. The compensation capability of a dynamic voltage restorer (DVR) depends primarily on the maximum voltage injection ability and the amount of stored energy available within the restorer. In this paper a new phase advance compensation (PAC) strategy for the DVR is proposed in order to enhance the voltage restoration property of the device. Also a method of determining the exact amount of voltage injection required to systematically correct a specific voltage drop with minimum active power injection is proposed. Using the proposed method it can be shown that a particular disturbance can be corrected with less amount of storage energy compared to that of existing in-phase boosting method. Analytical expressions for both the magnitude and angle of the injected voltage are also derived. A closed-loop controller that consists of an inner current loop and an outer voltage loop is also incorporated into the DVR system.