Home About us Contact
|
Home About us Contact | ||
|
|
||
Error Structure (error + structure)
Selected AbstractsA novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noisePROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 15 2008Deukwoo Kwon Dr. Abstract In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre-processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre-processing techniques used. One important pre-processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass specrometry data and show that it improves on performances of peak detection methods. [source] On inference for a semiparametric partially linear regression model with serially correlated errorsTHE CANADIAN JOURNAL OF STATISTICS, Issue 4 2007Jinhong You Abstract The authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference procedure consisting of a bandwidth selection method, an efficient semiparametric generalized least squares estimator of the parametric component, a goodness-of-fit test based on the bootstrap, and a technique for selecting significant covariates in the parametric component. They assess their approach through simulation studies and illustrate it with a concrete application. L'inférence dans le cadre d'un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série Les auteurs s'intéressent à un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série. Ils proposent une façon originale d'estimer la structure d'erreur qui a l'avantage de ne faire intervenir aucune estimation non paramétrique. Ceci leur permet de développer une procédure d'inférence comportant un choix de fen,tre, l'emploi de la méthode des moindres carrés généralisés pour l'estimation semiparamétrique efficace de la composante paramétrique, un test d'adéquation fondé sur le rééchantillonnage et une technique de sélection des covariables significatives de la composante paramétrique. Ils évaluent leur approche par voie de simulation et en donnent une illustration concrète. [source] Technical Inefficiency and Production Risk in Rice Farming: Evidence from Central Luzon Philippines,ASIAN ECONOMIC JOURNAL, Issue 1 2006Renato Villano Q12; C13 There have been many previous studies of technical inefficiency in rice production in the Philippines, but none has focused simultaneously on production risk and technical inefficiency at the farm level. Rice production is inherently risky because of the heterogeneous production environment. In this study, we analyze technical inefficiency in a rainfed lowland rice environment in Central Luzon using a stochastic frontier production function with a heteroskedastic error structure. An 8-year panel dataset collected from 46 rainfed rice farmers was used to estimate flexible functional specifications. Over the whole period, the average technical efficiency was found to be 79 percent. Results indicate that there is a high degree of variability in technical efficiency estimates, which can be attributed to the instability of farming conditions in the rainfed lowland environment. Mean output was signifificantly influenced by area planted to rice, labor and the amount of fertilizer used. Consequently, these inputs were found to be risk-increasing, whereas herbicide was found to be a risk-reducing input. [source] Prediction from an Integrated Regression Equation: A Forestry ApplicationBIOMETRICS, Issue 2 2000Timothy G. Gregoire Summary. Models to depict the tapering of a tree bole abound in the literature of forest science, and such models are widely used in forestry practice. One important use is the integration of a taper equation to predict the volume of the tree bole. The statistical properties of volume prediction from an integrated taper equation have been obscure. Based on the statistical characteristics of a taper model for the bole's cross-sectional area, we derive the first two moments of the volume predictor and the prediction error. Bias from the integration is nil. The importance of a reasonable model of the error structure is demonstrated. [source] Retailer's Response to Alternate Manufacturer's Incentives Under a Single-Period, Price-Dependent, Stochastic-Demand Framework,DECISION SCIENCES, Issue 4 2005F. J. Arcelus ABSTRACT This article considers the joint development of the optimal pricing and ordering policies of a profit-maximizing retailer, faced with (i) a manufacturer trade incentive in the form of a price discount for itself or a rebate directly to the end customer; (ii) a stochastic consumer demand dependent upon the magnitude of the selling price and of the trade incentive, that is contrasted with a riskless demand, which is the expected value of the stochastic demand; and (iii) a single-period newsvendor-type framework. Additional analysis includes the development of equal profit policies in either form of trade incentive, an assessment of the conditions under which a one-dollar discount is more profitable than a one-dollar rebate, and an evaluation of the impact upon the retailer-expected profits of changes in either incentive or in the degree of demand uncertainty. A numerical example highlights the main features of the model. The analytical and numerical results clearly show that, as compared to the results for the riskless demand, dealing with uncertainty through a stochastic demand leads to (i) (lower) higher retail prices if additive (multiplicative) error, (ii) lower (higher) pass throughs if additive (multiplicative) error, (iii) higher claw backs in both error structures wherever applicable, and (iv) higher rebates to achieve equivalent profits in both error structures. [source] Mathematical improvements to maximum likelihood parallel factor analysis: theory and simulationsJOURNAL OF CHEMOMETRICS, Issue 4 2005Lorenzo Vega-Montoto Abstract A number of simplified algorithms for carrying out maximum likelihood parallel factor analysis (MLPARAFAC) for three-way data affected by different error structures are described. The MLPARAFAC method was introduced to establish the theoretical basis to treat heteroscedastic and/or correlated noise affecting trilinear data. Unfortunately, the large size of the error covariance matrix employed in the general formulation of this algorithm prevents its application to solve standard three-way problems. The algorithms developed here are based on the principle of alternating least squares, but differ from the generalized MLPARAFAC algorithm in that they do not use equivalent alternatives of the objective function to estimate the loadings for the different modes. Instead, these simplified algorithms tackle the loss of symmetry of the PARAFAC model by using only one representation of the objective function to estimate the loadings of all of the modes. In addition, a compression step is introduced to allow the use of the generalized algorithm. Simulation studies carried out under a variety of measurement error conditions were used for statistical validation of the maximum likelihood properties of the algorithms and to assess the quality of the results and computation time. The simplified MLPARAFAC methods are also shown to produce more accurate results than PARAFAC under a variety of conditions. Copyright © 2005 John Wiley & Sons, Ltd. [source] A Bayesian Chi-Squared Goodness-of-Fit Test for Censored Data ModelsBIOMETRICS, Issue 2 2010Jing Cao Summary We propose a Bayesian chi-squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi-squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988,,Journal of the American Statistical Association,83, 222,230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures. [source] | ||||||||||||||||