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Estimated Parameters (estimated + parameter)
Selected AbstractsMoisture sorption characteristics of curd (Indian yogurt) powderINTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY, Issue 1 2009SHIBY VARGHESE K. The moisture sorption behaviour of curd (Indian yogurt) powder was studied at 20, 30, 40 and 50°C for water activity ranging from 0.07 to 0.85. GAB, BET, Henderson, Halsey, Chung & Pfost, Smith, Oswin and Peleg models were applied to analyse the data. Estimated parameters and fitting ability for sorption models were evaluated. The GAB model showed the best fit to the sorption data of curd powder at 20, 30 and 40°C, and the Peleg model fitted well at 50°C. [source] Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C3 photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (Triticum aestivum) canopyPLANT CELL & ENVIRONMENT, Issue 5 2009XINYOU YIN ABSTRACT We appraised the literature and described an approach to estimate the parameters of the Farquhar, von Caemmerer and Berry model using measured CO2 assimilation rate (A) and photosystem II (PSII) electron transport efficiency (,2). The approach uses curve fitting to data of A and ,2 at various levels of incident irradiance (Iinc), intercellular CO2 (Ci) and O2. Estimated parameters include day respiration (Rd), conversion efficiency of Iinc into linear electron transport of PSII under limiting light [,2(LL)], electron transport capacity (Jmax), curvature factor (,) for the non-rectangular hyperbolic response of electron flux to Iinc, ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco) CO2/O2 specificity (Sc/o), Rubisco carboxylation capacity (Vcmax), rate of triose phosphate utilization (Tp) and mesophyll conductance (gm). The method is used to analyse combined gas exchange and chlorophyll fluorescence measurements on leaves of various ages and positions in wheat plants grown at two nitrogen levels. Estimated Sc/o (25 °C) was 3.13 mbar µbar,1; Rd was lower than respiration in the dark; Jmax was lower and , was higher at 2% than at 21% O2; ,2(LL), Vcmax, Jmax and Tp correlated to leaf nitrogen content; and gm decreased with increasing Ci and with decreasing Iinc. Based on the parameter estimates, we surmised that there was some alternative electron transport. [source] On-line identification of non-linear hysteretic structural systems using a variable trace approachEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 9 2001Jeng-Wen Lin Abstract In this paper, an adaptive on-line parametric identification algorithm based on the variable trace approach is presented for the identification of non-linear hysteretic structures. At each time step, this recursive least-square-based algorithm upgrades the diagonal elements of the adaptation gain matrix by comparing the values of estimated parameters between two consecutive time steps. Such an approach will enforce a smooth convergence of the parameter values, a fast tracking of the parameter changes and will remain adaptive as time progresses. The effectiveness and efficiency of the proposed algorithm is shown by considering the effects of excitation amplitude, of the measurement units, of larger sampling time interval and of measurement noise. The cases of exact-, under-, over-parameterization of the structural model have been analysed. The proposed algorithm is also quite effective in identifying time-varying structural parameters to simulate cumulative damage in structural systems. Copyright © 2001 John Wiley & Sons, Ltd. [source] Spatial sampling design under the infill asymptotic framework,ENVIRONMETRICS, Issue 4 2006Zhengyuan Zhu Abstract We study optimal sample designs for prediction with estimated parameters. Recent advances in the infill asymptotic theory provide a deeper understanding of the finite sample behavior of prediction and estimation. By incorporating these known asymptotic results, we modify some existing design criteria for estimation of covariance function and best linear unbiased prediction. These modified criteria could significantly reduce the computation time necessary for finding an optimal design. We illustrate our approach through both a real experiment in agriculture and simulation. Copyright © 2005 John Wiley & Sons, Ltd. [source] Detecting genotype combinations that increase risk for disease: Maternal-Fetal genotype incompatibility testGENETIC EPIDEMIOLOGY, Issue 1 2003Janet S. Sinsheimer Abstract Biological mechanisms that involve gene-by-environment interactions have been hypothesized to explain susceptibility to complex familial disorders. Current research provides compelling evidence that one environmental factor, which acts prenatally to increase susceptibility, arises from a maternal-fetal genotype incompatibility. Because it is genetic in origin, a maternal-fetal incompatibility is one possible source of an adverse environment that can be detected in genetic analyses and precisely studied, even years after the adverse environment was present. Existing statistical models and tests for gene detection are not optimal or even appropriate for identifying maternal-fetal genotype incompatibility loci that may increase the risk for complex disorders. We describe a new test, the maternal-fetal genotype incompatibility (MFG) test, that can be used with case-parent triad data (affected individuals and their parents) to identify loci for which a maternal-fetal genotype incompatibility increases the risk for disease. The MFG test adapts a log-linear approach for case-parent triads in order to detect maternal-fetal genotype incompatibility at a candidate locus, and allows the incompatibility effects to be estimated separately from direct effects of either the maternal or the child's genotype. Through simulations of two biologically plausible maternal-fetal genotype incompatibility scenarios, we show that the type-I error rate of the MFG test is appropriate, that the estimated parameters are accurate, and that the test is powerful enough to detect a maternal-fetal genotype incompatibility of moderate effect size. Genet Epidemiol 24:1,13, 2003. © 2003 Wiley-Liss, Inc. [source] Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non-water-stressed conditionsGLOBAL CHANGE BIOLOGY, Issue 4 2007KATHERINE E. OWEN Abstract This paper examines long-term eddy covariance data from 18 European and 17 North American and Asian forest, wetland, tundra, grassland, and cropland sites under non-water-stressed conditions with an empirical rectangular hyperbolic light response model and a single layer two light-class carboxylase-based model. Relationships according to ecosystem functional type are demonstrated between empirical and physiological parameters, suggesting linkages between easily estimated parameters and those with greater potential for process interpretation. Relatively sparse documentation of leaf area index dynamics at flux tower sites is found to be a major difficulty in model inversion and flux interpretation. Therefore, a simplification of the physiological model is carried out for a subset of European network sites with extensive ancillary data. The results from these selected sites are used to derive a new parameter and means for comparing empirical and physiologically based methods across all sites, regardless of ancillary data. The results from the European analysis are then compared with results from the other Northern Hemisphere sites and similar relationships for the simplified process-based parameter were found to hold for European, North American, and Asian temperate and boreal climate zones. This parameter is useful for bridging between flux network observations and continental scale spatial simulations of vegetation/atmosphere carbon dioxide exchange. [source] The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statisticsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2009D. Maraun Abstract The annual cycle of extreme 1-day precipitation events across the UK is investigated by developing a statistical model and fitting it to data from 689 rain gauges. A generalized extreme-value distribution (GEV) is fit to the time series of monthly maxima, across all months of the year simultaneously, by approximating the annual cycles of the location and scale parameters by harmonic functions, while keeping the shape parameter constant throughout the year. We average the shape parameter of neighbouring rain gauges to decrease parameter uncertainties, and also interpolate values of all model parameters to give complete coverage of the UK. The model reveals distinct spatial patterns for the estimated parameters. The annual mean of the location and scale parameter is highly correlated with orography. The annual cycle of the location parameter is strong in the northwest UK (peaking in late autumn or winter) and in East Anglia (where it peaks in late summer), and low in the Midlands. The annual cycle of the scale parameter exhibits a similar pattern with strongest amplitudes in East Anglia. The spatial patterns of the annual cycle phase suggest that they are linked to the dominance of frontal precipitation for generating extreme precipitation in the west and convective precipitation in the southeast of the UK. The shape parameter shows a gradient from positive values in the east to negative values in some areas of the west. We also estimate 10-year and 100-year return levels at each rain gauge, and interpolated across the UK. Copyright © 2008 Royal Meteorological Society [source] Meta-analysis in model implementation: choice sets and the valuation of air quality improvementsJOURNAL OF APPLIED ECONOMETRICS, Issue 6 2007H. Spencer Banzhaf This research illustrates how the methods developed for meta-analysis can serve to document and summarize voluminous information derived from repeated sensitivity analyses. Our application is to the sensitivity of welfare estimates derived from discrete choice models to assumptions about the choice set. These assumptions affect welfare estimates through both the estimated parameters of the model and, conditional on the parameters, the substitution among alternatives. In our specific application, the evaluation is in terms of estimated benefits of air quality improvements in Los Angeles based on discrete choices of neighborhood and housing. Copyright © 2007 John Wiley & Sons, Ltd. [source] Age and growth, mortality, reproduction and relative yield per recruit of the bogue, Boops boops Linné, 1758 (Sparidae), from the Algarve (south of Portugal) longline fisheryJOURNAL OF APPLIED ICHTHYOLOGY, Issue 5 2006P. Monteiro Summary Samples of Boops boops ranging from 7.4 to 30.5 cm were obtained mainly by longline, supplemented by beach seining in the Ria Formosa lagoon, and by market sampling in the Algarve (southern Portugal). The macroscopic analyses of the gonads and the gonad somatic index showed that the south coast of Portugal B. boops spawn mainly from late winter to spring, between February and May. The length at first maturity was similar for males and females and the value for both sexes combined was estimated to be 15.22 cm, corresponding to an age range of 1,3. Age was determined by reading growth bands on otoliths. Age determination was validated by marginal increment analysis. The estimated parameters were L, = 28.06, K = 0.22 and t0 = ,1.42. Mortality rates were calculated for fish captured with longlines, and the estimated parameters were M = 0.33, Z = 1.04 and F = 0.71. Relative yield per recruit analysis and sensitivity analysis showed that the resource is moderately exploited. From the perspective of sustainability, these results provide support for the use of longlines as a gear that is among the least harmful for species such as the bogue. [source] An efficient nonlinear programming strategy for PCA models with incomplete data setsJOURNAL OF CHEMOMETRICS, Issue 6 2010Rodrigo López-Negrete de la Fuente Abstract Processing plants can produce large amounts of data that process engineers use for analysis, monitoring, or control. Principal component analysis (PCA) is well suited to analyze large amounts of (possibly) correlated data, and for reducing the dimensionality of the variable space. Failing online sensors, lost historical data, or missing experiments can lead to data sets that have missing values where the current methods for obtaining the PCA model parameters may give questionable results due to the properties of the estimated parameters. This paper proposes a method based on nonlinear programming (NLP) techniques to obtain the parameters of PCA models in the presence of incomplete data sets. We show the relationship that exists between the nonlinear iterative partial least squares (NIPALS) algorithm and the optimality conditions of the squared residuals minimization problem, and how this leads to the modified NIPALS used for the missing value problem. Moreover, we compare the current NIPALS-based methods with the proposed NLP with a simulation example and an industrial case study, and show how the latter is better suited when there are large amounts of missing values. The solutions obtained with the NLP and the iterative algorithm (IA) are very similar. However when using the NLP-based method, the loadings and scores are guaranteed to be orthogonal, and the scores will have zero mean. The latter is emphasized in the industrial case study. Also, with the industrial data used here we are able to show that the models obtained with the NLP were easier to interpret. Moreover, when using the NLP many fewer iterations were required to obtain them. Copyright © 2010 John Wiley & Sons, Ltd. [source] A new efficient method for determining the number of components in PARAFAC modelsJOURNAL OF CHEMOMETRICS, Issue 5 2003Rasmus Bro Abstract A new diagnostic called the core consistency diagnostic (CORCONDIA) is suggested for determining the proper number of components for multiway models. It applies especially to the parallel factor analysis (PARAFAC) model, but also to other models that can be considered as restricted Tucker3 models. It is based on scrutinizing the ,appropriateness' of the structural model based on the data and the estimated parameters of gradually augmented models. A PARAFAC model (employing dimension-wise combinations of components for all modes) is called appropriate if adding other combinations of the same components does not improve the fit considerably. It is proposed to choose the largest model that is still sufficiently appropriate. Using examples from a range of different types of data, it is shown that the core consistency diagnostic is an effective tool for determining the appropriate number of components in e.g. PARAFAC models. However, it is also shown, using simulated data, that the theoretical understanding of CORCONDIA is not yet complete. Copyright © 2003 John Wiley & Sons, Ltd. [source] An Examination of Rater Drift Within a Generalizability Theory FrameworkJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 1 2009Polina Harik The present study examined the long-term usefulness of estimated parameters used to adjust the scores from a performance assessment to account for differences in rater stringency. Ratings from four components of the USMLE® Step 2 Clinical Skills Examination data were analyzed. A generalizability-theory framework was used to examine the extent to which rater-related sources of error could be eliminated through statistical adjustment. Particular attention was given to the stability of these estimated parameters over time. The results suggest that rater stringency estimates obtained at a point in time and then used to adjust ratings over a period of months may substantially decrease in usefulness. In some cases, over several months, the use of these adjustments may become counterproductive. Additionally, it is hypothesized that the rate of deterioration in the usefulness of estimated parameters may be a function of the characteristics of the scale. [source] Asymptotic prediction of mean squared error for long-memory processes with estimated parametersJOURNAL OF FORECASTING, Issue 8 2008Naoya Katayama Abstract In this paper we deal with the prediction theory of long-memory time series. The purpose is to derive a general theory of the convergence of moments of the nonlinear least squares estimator so as to evaluate the asymptotic prediction mean squared error (PMSE). The asymptotic PMSE of two predictors is evaluated. The first is defined by the estimator of the differencing parameter, while the second is defined by a fixed differencing parameter: in other words, a parametric predictor of the seasonal autoregressive integrated moving average model. The effects of misspecifying the differencing parameter is a long-memory model are clarified by the asymptotic results relating to the PMSE. The finite sample behaviour of the predictor and the model selection in terms of PMSE of the two predictors are examined using simulation, and the source of any differences in behaviour made clear in terms of asymptotic theory. Copyright © 2008 John Wiley & Sons, Ltd. [source] Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRIJOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 1 2009Anup Singh PhD Abstract Purpose To develop a methodology for improved estimation of bolus arrival time (BAT) and arterial input function (AIF) which are prerequisites for tracer kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and to verify the applicability of the same in the case of intracranial lesions (brain tumor and tuberculoma). Materials and Methods A continuous piecewise linear (PL) model (with BAT as one of the free parameters) is proposed for concentration time curve C(t) in T1 -weighted DCE-MRI. The resulting improved procedure suggested for automatic extraction of AIF is compared with earlier methods. The accuracy of BAT and other estimated parameters is tested over simulated as well as experimental data. Results The proposed PL model provides a good approximation of C(t) trends of interest and fit parameters show their significance in a better understanding and classification of different tissues. BAT was correctly estimated. The automatic and robust estimation of AIF obtained using the proposed methodology also corrects for partial volume effects. The accuracy of tracer kinetic analysis is improved and the proposed methodology also reduces the time complexity of the computations. Conclusion The PL model parameters along with AIF measured by the proposed procedure can be used for an improved tracer kinetic analysis of DCE-MRI data. J. Magn. Reson. Imaging 2009;29:166,176. © 2008 Wiley-Liss, Inc. [source] Municipal sludge degradation kinetic in thermophilic CSTRAICHE JOURNAL, Issue 12 2006Ángeles de la Rubia Abstract The performance of a pilot-scale continuous-flow stirred-tank reactor (CSTR) treating municipal sludge under thermophilic conditions has been studied. Two pilot-scale reactors (CSTR1 (175 L) and CSTR2 (850 L)) were operated at different hydraulic residence times (,: 40 to 15 days). The anaerobic sludge processes are generally affected by variations in the concentration of substrate (determined as influent volatile solids, VS) and volumetric flow, both of which lead to a modification in biomass concentration and VS removal efficiency. This unsteady-state situation is mathematically explained in terms of an autocatalytic kinetic model. The general kinetic equation in this model has been applied to experimental data obtained in CSTR1. The fit of the experimental data to the model was used to estimate kinetic parameters and the yield coefficients (,max, ,, YP/S). The estimated parameters were ,max: 0.175d,1, ,: 0.358, YP/S: 0.309 m3CH4/kgVS). These parameters were subsequently used to model the substrate utilization rate and the methane generation rate in CSTR2. The model with the estimated parameters was found to provide excellent results, and is satisfactory in describing the concentration of VS and the methane generation rate in an actual digestion plant. © 2006 American Institute of Chemical Engineers AIChE J, 2006 [source] Moment estimation for statistics from marked point processesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2001Dimitris N. Politis In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L2 -consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated parameters. A practical data-based method for choosing a subsampling parameter is given and illustrated on a data set. Finite sample simulation examples are also presented. [source] Treating missing values in INAR(1) models: An application to syndromic surveillance dataJOURNAL OF TIME SERIES ANALYSIS, Issue 1 2010Jonas Andersson Time-series models for count data have found increased interest in recent years. The existing literature refers to the case of data that have been fully observed. In this article, methods for estimating the parameters of the first-order integer-valued autoregressive model in the presence of missing data are proposed. The first method maximizes a conditional likelihood constructed via the observed data based on the k -step-ahead conditional distributions to account for the gaps in the data. The second approach is based on an iterative scheme where missing values are imputed so as to update the estimated parameters. The first method is useful when the predictive distributions have simple forms. We derive in full details this approach when the innovations are assumed to follow a finite mixture of Poisson distributions. The second method is applicable when there are no closed form expression for the conditional likelihood or they are hard to derive. The proposed methods are applied to a dataset concerning syndromic surveillance during the Athens 2004 Olympic Games. [source] A light-tailed conditionally heteroscedastic model with applications to river flowsJOURNAL OF TIME SERIES ANALYSIS, Issue 1 2008Péter Elek Abstract., A conditionally heteroscedastic model, different from the more commonly used autoregressive moving average,generalized autoregressive conditionally heteroscedastic (ARMA-GARCH) processes, is established and analysed here. The time-dependent variance of innovations passing through an ARMA filter is conditioned on the lagged values of the generated process, rather than on the lagged innovations, and is defined to be asymptotically proportional to those past values. Designed this way, the model incorporates certain feedback from the modelled process, the innovation is no longer of GARCH type, and all moments of the modelled process are finite provided the same is true for the generating noise. The article gives the condition of stationarity, and proves consistency and asymptotic normality of the Gaussian quasi-maximum likelihood estimator of the variance parameters, even though the estimated parameters of the linear filter contain an error. An analysis of six diurnal water discharge series observed along Rivers Danube and Tisza in Hungary demonstrates the usefulness of such a model. The effect of lagged river discharge turns out to be highly significant on the variance of innovations, and nonparametric estimation approves its approximate linearity. Simulations from the new model preserve well the probability distribution, the high quantiles, the tail behaviour and the high-level clustering of the original series, further justifying model choice. [source] Patch dynamics in a landscape modified by ecosystem engineersOIKOS, Issue 2 2004Justin P. Wright Ecosystem engineers, organisms that modify the environment, have the potential to dramatically alter ecosystem structure and function at large spatial scales. The degree to which ecosystem engineering produces large-scale effects is, in part, dependent on the dynamics of the patches that engineers create. Here we develop a set of models that links the population dynamics of ecosystem engineers to the dynamics of the patches that they create. We show that the relative abundance of different patch types in an engineered landscape is dependent upon the production of successful colonists from engineered patches and the rate at which critical resources are depleted by engineers and then renewed. We also consider the effects of immigration from either outside the system or from engineers that are present in non-engineered patches, and the effects of engineers that can recolonize patches before they are fully recovered on the steady state distribution of different patch types. We use data collected on the population dynamics of a model engineer, the beaver, to estimate the per-patch production rate of new colonists, the decay rate of engineered patches, and the recovery rate of abandoned patches. We use these estimated parameters as a baseline to determine the effects of varying parameters on the distribution of different patch types. We suggest a number of hypotheses that derive from model predictions and that could serve as tests of the model. [source] In Vivo Pharmacokinetics of ,-Aminolevulinic Acid,Induced Protoporphyrin IX During Pre- and Post-Photodynamic Therapy in 7,12-Dimethylbenz(a)nthracene-Treated Skin Carcinogenesis in Swiss Mice: A Comparison by Three-Compartment Model,,PHOTOCHEMISTRY & PHOTOBIOLOGY, Issue 1 2002Parmeswaran Diagaradjane ABSTRACT ,-Aminolevulinic acid,photodynamic therapy (ALA-PDT) has emerged as a useful technique in the treatment of superficial basal cell carcinoma, actinic keratosis, squamous cell carcinoma and tumors of other organs. Earlier reports mention that there is reappearance of protoporphyrin IX (PpIX) after photoirradiation of tumors. This property of reappearance of PpIX is being utilized to treat nodular tumors by fractionated light dose delivery. However, there is still no unanimously accepted reason for this reappearance phenomenon and the rate of resynthesis after PDT. On account of this, studies are carried out on the estimation of the pharmacokinetics of the ALA-induced PpIX in mice tumor models and the surrounding normal tissues before and after PDT. Further, a mathematical model based on a multiple compartment system is proposed to estimate the rate parameter for the diffusion of PpIX from the surrounding normal tissues into the tumor tissue (km) caused by photobleaching during PDT with irradiating fluences of 36.0 and 57.6 J/cm2. The km value at two different fluences, 36.0 and 57.6 J/cm2, are estimated as 3.0636 ± 0.7083 h,1 and 6.9231 ± 2.17651 h,1, respectively. Further, the rate parameter for the cleavage and efflux of ALA (k1) and the rate parameter for the evasion of PpIX from the tumor tissues after PDT (kt) were also estimated by fitting the experimental data to the developed mathematical model. The statistical significance of the estimated parameters was determined using Student's t -test. The experimental results and the rate parameters obtained using the proposed compartment model suggest that in addition to the earlier reported reasons, the invasion or diffusion of PpIX from the surrounding tissues to the tumor tissues after photoirradiation might also contribute to the reappearance of PpIX after PDT. [source] Association among growth, food consumption-related traits and amylase gene polymorphism in the Pacific oyster Crassostrea gigasANIMAL GENETICS, Issue 6 2008A. Huvet Summary To examine further a previously reported association between amylase gene polymorphism and growth in the Pacific oyster Crassostrea gigas, ecophysiological parameters and biochemical and molecular expression levels of ,-amylase were studied in Pacific oysters of different amylase genotypes. Genotypes that previously displayed significantly different growth were found to be significantly different for ingestion and absorption efficiency. These estimated parameters, used in a dynamic energy budget model, showed that observed ingestion rates (unlike absorption efficiencies) allowed an accurate prediction of growth potential in these genotypes. The observed association between growth and amylase gene polymorphism is therefore more likely to be related to ingestion than to absorption efficiency. Additionally, relative mRNA levels of the two amylase cDNAs were also strongly associated with amylase gene polymorphism, possibly reflecting variation in an undefined regulatory region, although no corresponding variation was observed in specific amylase activity. Amylase gene sequences were determined for each genotype, showing the existence of only synonymous or functionally equivalent non-synonymous polymorphisms. The observed associations among growth, food consumption-related traits and amylase gene polymorphism are therefore more likely to be related to variation in the level of amylase gene expression than to functional enzymatic variants. [source] Stabilization of uncertain chained nonholonomic systems using adaptive output feedback,ASIAN JOURNAL OF CONTROL, Issue 6 2009Z. P. Yuan Abstract In this paper, adaptive output feedback control is presented to solve the stabilization problem of nonholonomic systems in chained form with strong nonlinear drifts and uncertain parameters using output signals only. The objective is to design adaptive nonlinear output feedback laws which can steer the closed-loop systems to globally converge to the origin, while the estimated parameters remain bounded. The proposed systematic strategy combines input-state scaling with backstepping technique. Motivated from a special case, adaptive output feedback controllers are proposed for a class of uncertain chained systems. The simulation results demonstrate the effectiveness of the proposed controllers. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] An Adaptive Single-step FDR Procedure with Applications to DNA Microarray AnalysisBIOMETRICAL JOURNAL, Issue 1 2007Vishwanath Iyer Abstract The use of multiple hypothesis testing procedures has been receiving a lot of attention recently by statisticians in DNA microarray analysis. The traditional FWER controlling procedures are not very useful in this situation since the experiments are exploratory by nature and researchers are more interested in controlling the rate of false positives rather than controlling the probability of making a single erroneous decision. This has led to increased use of FDR (False Discovery Rate) controlling procedures. Genovese and Wasserman proposed a single-step FDR procedure that is an asymptotic approximation to the original Benjamini and Hochberg stepwise procedure. In this paper, we modify the Genovese-Wasserman procedure to force the FDR control closer to the level alpha in the independence setting. Assuming that the data comes from a mixture of two normals, we also propose to make this procedure adaptive by first estimating the parameters using the EM algorithm and then using these estimated parameters into the above modification of the Genovese-Wasserman procedure. We compare this procedure with the original Benjamini-Hochberg and the SAM thresholding procedures. The FDR control and other properties of this adaptive procedure are verified numerically. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] | |||||||||||||||||||||||||