Error Distribution Model Research Articles

Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis

Column selection row parity (CPRS) diagnosis is an X-tolerant and low aliasing technique that is suitable for the BIST environment. A row selection LFSR randomly selects outputs of multiple scan chains so that unknowns can be tolerated. Column and row parities of selected outputs are observed to solve linear equations for the error positions. Experimental data show that CPRS achieves nearly perfect diagnosis, even in the presence of 1 percent unknowns. CPRS compresses the diagnosis data because only parities of circuit responses, instead of responses themselves, are observed. Two error distribution models (scattered and clustered) are developed and analyzed to show the effectiveness of CPRS. The analytical results are demonstrated to be accurate by more than 10,000 experiments

Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part 2. Chlorophyll-a, nutrients and SPM

Marine systems models are becoming increasingly complex and sophisticated, but far too little attention has been paid to model errors and the extent to which model outputs actually relate to ecosystem processes. Here we describe the application of summary error statistics to a complex 3D model (POLCOMS-ERSEM) run for the period 1988–1989 in the southern North Sea utilising information from the North Sea Project, which collected a wealth of observational data. We demonstrate that to understand model data misfit and the mechanisms creating errors, we need to use a hierarchy of techniques, including simple correlations, model bias, model efficiency, binary discriminator analysis and the distribution of model errors to assess model errors spatially and temporally. We also demonstrate that a linear cost function is an inappropriate measure of misfit. This analysis indicates that the model has some skill for all variables analysed. A summary plot of model performance indicates that model performance deteriorates as we move through the ecosystem from the physics, to the nutrients and plankton.

Performance analysis of a file delivery system based on the FLUTE protocol

AbstractThis paper presents the results of performance tests carried out for a file delivery system based on the file delivery over unidirectional transport (FLUTE) protocol. FLUTE is a file transport protocol used to deliver files over IP networks, including the Internet and unidirectional systems, from a sender to one or more receivers. FLUTE uses UDP, an unreliable transport protocol, and so reliable delivery must be guaranteed by other means. This paper shows how FLUTE manages to recover from packet losses using forward path redundancy (forward error correction (FEC) and repeat transmissions in a data carousel), and a simple HTTP‐based point‐to‐point file repair scheme which is specified in 3GPP and DVB standards. The results presented in this paper show that careful optimization of FEC overhead, and the number of repeat transmissions, gives the best system performance in most cases. Based on the simplified error reception and distribution model depicted in this study, it is illustrated that the simple client‐server point‐to‐point file repair is optimal only for small groups. Several options to improve the configuration of FLUTE senders are provided, to deliver reception guarantees with optimal data expense from the system point of view. Copyright © 2006 John Wiley & Sons, Ltd.

Analog behavioral modeling by multi-companding and wavelet collocation method

In this paper, we develop a wavelet collocation method with multi-companding for behavioral modeling of analog circuits. In the multi-companding procedure, the nonlinear companding algorithm is developed to control the error distribution continuously, while the adaptive scheme is employed to reduce the number of used wavelets. Consequently, the proposed multi-companding algorithm can not only modify the modeling error distribution continuously but also decrease the number of basis functions efficiently. Moreover, the companding function generation is automatic and can be applied for the behavioral modeling of any analog circuits.

EyeCerts

In this paper, we propose EyeCerts, a biometric system for the identification of people which achieves offline verification of certified, cryptographically secure documents. An EyeCert is a printed document which certifies the association of content on the document with a biometric feature-a compressed version of a human iris in this work. The system is highly cost-effective since it does not require high complexity, hard-to-replicate printing technologies. Further, the device used to verify an EyeCert is inexpensive, estimated to have approximately the same cost as an off-the-shelf iris-scanning camera. As a central component of the EyeCert system, we present an iris analysis technique that aims to extract and compress the unique features of a given iris with a discrimination criterion using limited storage. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: 1) the algorithm detects the human iris by using a new model which is able to compensate for the noise introduced by the surrounding eyelashes and eyelids, 2) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and 3) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low-quality imaging system (sub-U.S.$100), a /spl chi//sup 2/ error distribution model, and assuming a fixed false negatives rate of 5%, EyeCert caused false positives at rates better than 10/sup -5/ and as low as 10/sup -30/ for certain users.

A calibration method for estimating absolute expression levels from microarray data

We describe an approach to normalize spotted microarray data, based on a physically motivated calibration model. This model consists of two major components, describing the hybridization of target transcripts to their corresponding probes on the one hand, and the measurement of fluorescence from the hybridized, labeled target on the other hand. The model parameters and error distributions are estimated from external control spikes. Using a publicly available dataset, we show that our procedure is capable of adequately removing the typical non-linearities of the data, without making any assumptions on the distribution of differences in gene expression from one biological sample to the next. Since our model links target concentration to measured intensity, we show how absolute expression values of target transcripts in the hybridization solution can be estimated up to a certain degree.

Alternative error distribution models for standardization of catch rates of non-target species from a pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery

Alternative error distributions were evaluated for calculating indices of relative abundance for non-target species using catch and effort data from commercial fisheries. A general procedure is presented for testing the underlying assumptions of different error distributions. Catch rates, from an observer program, of billfish caught mainly as bycatch in a pelagic tuna longline fishery in the Western Central Atlantic were standardized. Although catches of billfishes are not common in pelagic tuna longline fisheries, these fisheries are one of the main sources of fishing mortality for these stocks in the central Atlantic due to the magnitude and spatial extent of longline fishing effort. Billfish CPUE data are highly skewed with a large proportion of zero observations. Delta distribution models can accommodate this type of data, and involve modeling the probability of a non-zero observation and the catch rate given that the catch is non-zero separately. Three different Delta models were compared against other error distributions, including the lognormal, log-gamma, and Poisson. Diagnostic checks and deviance table analyses were performed to identify the best error distribution and the set of factors and interactions that most adequately explained the observed variability. The results indicated that the Delta-lognormal model (a binomial error distribution for the probability of a non-zero catch and lognormal error for the positive catch rates) complied best with the underlying characteristics of the data set. Analyses of catch rates for blue marlin, white marlin and sailfish confirmed the spatio-temporal nature of their distribution in the central Atlantic and Caribbean Sea. Also, the analyses indicated that catch rates of billfish differed among fishing vessel types; larger vessels had a higher probability of catching blue marlin, the more oceanic-oriented species, and lower probabilities of catching the more coastal-oriented species white marlin and sailfish. Standardized catch rates indicated in general a lower relative abundance for blue and white marlin in the most recent years, although estimated confidence intervals overlap through the years especially for white marlin.

An analysis of error structure in modeling the stock–recruitment data of gadoid stocks using generalized linear models

When modeling the stock–recruitment (S–R) relationship, the Cushing, Ricker, and other S–R models are fitted to the observed S–R data by estimating parameters with assumptions made concerning the model error structure. Using a generalized linear model approach, we explored and identified the appropriate model error structure in modeling S–R data for gadoid stocks. The S–R parameter estimation was found to be influenced by the choice of error distributions assumed in the analysis. In modeling S–R data for gadoid stocks, the Beverton–Holt model was found to be more sensitive to the assumption of model error distribution than the Cushing and Ricker models. The lognormal and gamma distributions had higher probability of being acceptable model error distributions. Cluster analyses and summary statistics of error distributions in S–R modeling did not show consistent patterns in the identification of an acceptable model error structure among species, geographic distributions, and sample sizes. A better understanding of the factors and mechanisms resulting in differences in the choice of appropriate model error distributions for different populations is needed in future research. We recommend that the generalized linear model be used to identify acceptable model error structures in quantifying S–R relationships.

Multiseason Verification of the MM5. Part I: Comparison with the Eta Model over the Central and Eastern United States and Impact of MM5 Resolution

Abstract This paper describes the multiseason verification of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the National Centers for Environmental Prediction (NCEP) Eta Model over the eastern two-thirds of the United States and surrounding coastal waters during the cool (1 November–31 March) and warm (1 May–30 September) seasons from the autumn of 1999 through the summer of 2001. Verification statistics are calculated by interpolating model forecasts to the observation sites. The horizontal and vertical distributions of model errors are presented as are the diurnal and intraseasonal trends. During the cool season, both the MM5 and Eta have a low-level cool and moist bias over land, a significant surface warm bias over water, and surface winds that are too strong over land to the east of the Rockies and too weak over water. The low-level cool and moist bias is maximized during the day, and the cool bias is largest during late winter. D...

Behavioral modeling for analog system-level simulation by wavelet collocation method

In this paper, we propose a wavelet collocation method with nonlinear companding to generate behavioral models for analog circuits at the system level. During the overall process of circuit modeling, nonlinear function approximation is an important issue to accurately capture the nonideal input-output relations of analog circuit blocks. While a great number of previous research works focus on the high-dimensional top-down design/synthesis model, which involves large analog design spaces, this paper primarily concentrates on the bottom-up verification model requiring both simple representation and high accuracy. Taking advantage of the local support of wavelet bases, a nonlinear companding method is developed to control the modeling error distribution based on system-level simulation requirements. It, in turn, significantly improves the simulation efficiency at the system level. To demonstrate the promising features of the proposed method, two circuit examples, a fourth-order switched-current filter and a voltage-controlled oscillator, are employed to build the behavioral models.

Incorporating a GED Model into the Historical Simulation Method for Value-at-Risk

This study proposes a procedure for using a generalized error distribution (GED) model in conjunction with historical simulation when computing Value-at-Risk (VaR). We collected data from the New York S&P 500, the London Times FTSE 100 and the Frankfurt Index to evaluate the VaR approaches. The sample period is from January 1, 1990 to December 31, 2001. Based on analysis of conservativeness, accuracy and efficiency, we show that incorporating GED model updating into historical simulation method is a substantial improvement.

Calibration Regression of Censored Lifetime Medical Cost

Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates; however, the bivariate model error distribution is completely unspecified. Whereas this conceptually simple model is a natural multivariate generalization of the accelerated failure time model, this article proposes an inference procedure by extending the weighted log-rank estimating function to the marked point process framework. The resulting estimators are consistent and asymptotically normal. In addition, robustness and efficiency issues are discussed. Furthermore, a novel sample-based variance estimation procedure is proposed for estimators based on nonsmooth estimating functions in general. Finally, simulation studies show that this proposal is reliable for practical use. An illustration with application to a lung cancer clinical trial is provided.

Political competition: theory and applications

Preface Introduction 1. Political Competition over a Single Issue: The Case of Certainty 1.1 Citizens, Voters, and Parties 1.2 The Downs Model 1.3 The Wittman Model 1.4 Conclusion 2. Modeling Party Uncertainty 2.1 Introduction 2.2 The State-Space Approach to Uncertainty 2.3 An Error-Distribution Model of Uncertainty 2.4 A Finite-Type Model 2.5 Conclusion 3. Unidimensional Policy Spaces with Uncertainty 3.1 Introduction 3.2 The Downs Model 3.3 The Wittman Model: An Example 3.4 Existence of Wittman Equilibrium 3.5 Properties of Wittman Equilibrium 3.6 Summary 4. Applications of the Wittman Model 4.1 Simple Models of Redistribution: The Politics of Extremism 4.2 Politico-Economic Equilibrium with Labor-Supply Elasticity 4.3 Partisan Dogmatism and Political Extremism 4.4 A Dynamic Model of Political Cycles 4.5 Conclusion 5. Endogenous Parties: The Unidimensional Case 5.1 Introduction 5.2 Average-Member Nash Equilibrium 5.3 Condorcet-Nash Equilibrium 5.4 Conclusion 6. Political Competition over Several Issues: The Case of Certainty 6.1 Introduction 6.2 The Downs Model 6.3 The Wittman Model 6.4 Conclusion 7. Multidimensional Issue Spaces and Uncertainty: The Downs Model 7.1 Introduction 7.2 The State-Space and Error-Distribution Models of Uncertainty 7.3 The Coughlin Model 7.4 The Lindbeck-Weibull Model 7.5 Adapting the Coughlin Model to the Case of Aggregate Uncertainty 7.6 Conclusion 8. Party Factions and Nash Equilibrium 8.1 Introduction 8.2 Party Factions 8.3 PUNE as a Bargaining Equilibrium 8.4 A Differential Characterization of PUNE 8.5 Regular Wittman Equilibrium 8.6 PUNEs in the Unidimensional Model 8.7 PUNEs in a Multidimensional Euclidean Model 8.8 Conclusion 9. The Democratic Political Economy of Progressive Taxation 9.1 Introduction 9.2 The Model 9.3 The Equilibrium Concepts 9.4 Analysis of Party Competition 9.5 Calibration 9.6 Conclusion 10. Why the Poor Do Not Expropriate the Rich in Democracies 10.1 The Historical Issue and a Model Preview 10.2 The Politico-Economic Environment 10.3 Analysis of PUNEs 10.4 Empirical Tests 10.5 Proofs of Theorems 10.6 Concluding Remark 11. Distributive Class Politics and the Political Geography of Interwar Europe 11.1 Introduction 11.2 The Luebbert Model 11.3 Testing Luebbert's Theory 11.4 Introducing the Communists: A Three-Party Model 11.5 Conclusion 11.6 Methodological Coda Appendix 11A 12. A Three-Class Model of American Politics 12.1 Introduction 12.2 The Model 12.3 Characterization of PUNEs 12.4 Results 12.5 Conclusion 13. Endogenous Parties with Multidimensional Competition 13.1 Introduction 13.2 Endogenous Parties 13.3 Taxation and Race 13.4 Fitting the Model to U.S. Data 13.5 Quadratic Taxation 13.6 Private Financing of Parties 13.7 A Technical Remark on the Existence of PUNEs 13.8 Conclusion 13.9 Why the Poor Do Not Expropriate the Rich: Reprise 14. Toward a Model of Coalition Government 14.1 Introduction 14.2 The Payoff Function of a Wittman Party 14.3 An Example of Coalition Government: Unidimensional Wittman Equilibrium 14.4 Multidimensional Three-Party Politics 14.5 Coalition Government with a Multidimensional Issue Space: An Example 14.6 Conclusion Mathematical Appendix A.1 Basics of Probability Theory A.2 Some Concepts from Analysis References Index

Robust performance optimization of open loop type problems using models from standard identification

This paper discusses the problem of finding the controller that optimizes the expected H 2-norm for an uncertain system. The paper gives a unification of many similar results. The approach is motivated by the so called stochastic embedding approach. A closed form solution is given using a minimum of calculations for a class of problems including interesting signal processing applications such as feedforward design, channel equalization, noise cancellation and signal filtering. The method uses covariance information on model uncertainty and can therefore be used together with standard identification methods. By using the probability distribution of model error we avoid the conservativeness related to designing for worst cases. We then obtain robust designs with soft bounds. It is shown how the optimal controller can be found by rewriting the problem as a standard H 2-problem for an extended system. The solution can hence be obtained using standard methods and software. The paper uses restrictions on where uncertain parameters enter into the system. Such restrictions are inevitable if soft bounds on parameters are used. The method has direct applications in adaptive signal processing and adaptive feedforward control.

航法性能要件と平行経路間間隔の関係に関する一考察

The required navigation performance (RNP) is a new concept which describes the navigation accuracy required for an aircraft when flying within a specified airspace. This paper first examines the method proposed by Flax et al. for deriving RNP values based on the double double exponential (DDE) distribution in relation to the route spacing. Then the consideration on RNP values for a different error distribution model, i.e., N-DE model (consists of the Gaussian core and the double exponential tail) is made. The results of the calculation based on the method indicate that a possible RNP value for the N-DE model differs considerably from the value obtained using the DDE model. This result suggests that the model selection for the core distribution should be carefully made when determining RNP values for oceanic airspace based on the method.

Development of Bayesian Monte Carlo techniques for water quality model uncertainty

A new technique, Bayesian Monte Carlo (BMC), is used to quantify errors in water quality models caused by uncertain parameters. BMC also provides estimates of parameter uncertainty as a function of observed data on model state variables. The use of Bayesian inference generates uncertainty estimates that combine prior information on parameter uncertainty with observed variation in water quality data to provide an improved estimate of model parameter and output uncertainty. It also combines Monte Carlo analysis with Bayesian inference to determine the ability of random selected parameter sets to simulate observed data. BMC expands upon previous studies by providing a quantitative estimate of parameter acceptability using the statistical likelihood function. The likelihood of each parameter set is employed to generate an n-dimensional hypercube describing a probability distribution of each parameter and the covariance among parameters. These distributions are utilized to estimate uncertainty in model predictions. Application of BMC to a dissolved oxygen model reduced the estimated uncertainty in model output by 72% compared with standard Monte Carlo techniques. Sixty percent of this reduction was directly attributed to consideration of covariance between model parameters. A significant benefit of the technique is the ability to compare the reduction in total model output uncertainty corresponding to: (1) collection of more data on model state variables, and (2) laboratory or field studies to better define model processes. Limitations of the technique include computational requirements and accurate estimation of the joint probability distribution of model errors. This analysis was conducted assuming that model error is normally and independently distributed.

Microcomputer-Based Simulation of Innovation Diffusion

A general-purpose microcomputer-based simulation model of the diffusion of innovation is described. The model employs a network data structure to emulate connections between adopters, and allows one to generate a network from any two variables. Methods for analyzing the accuracy and distribution of model errors are also presented. The diffusion of prohibition from 1876 to 1919 is simulated. Keywords: diffusion, simulation model, prohibition, network data structure.

Analyzing Numerical Errors in Domain Heat Transport Models Using the CVBEM

Besides providing an exact solution for steady-state heat conduction processes (Laplace-Poisson equations), the CVBEM (complex variable boundary element method) can be used for the numerical error analysis of domain model solutions. For problems where soil-water phase change latent heat effects dominate the thermal regime, heat transport can be approximately modeled as a time-stepped steady-state condition in the thawed and frozen regions, respectively. The CVBEM provides an exact solution of the two-dimensional steady-state heat transport problem, and also provides the error in matching the prescribed boundary conditions by the development of a modeling error distribution or an approximate boundary generation. Consequently, this error evaluation can be used to develop highly accurate CVBEM models of the heat transport process, and the resulting model can be used as a test case for evaluating the precision of domain models based on finite elements or finite differences.