Simultaneous Estimation of System and Input Parameters from Output Measurements

System identification of very large structures is of necessity accomplished by analyzing output measurements, as in the case of ambient vibration surveys. Conventional techniques typically identify system parameters by assuming (arguably) that the input is locally Gaussian white, and in so doing, effectively reduce the number of degrees of freedom of the estimation problem to a more tractable number. This paper describes a new approach that has several novel attributes, among them, elimination of the need for the Gaussian white input assumption. The approach involves a filter applied to an identification problem formulated in the frequency domain. The filter simultaneously estimates both system parameters and input excitation characteristics. The estimates we obtain are not guaranteed to be unique (as is true in all other approaches: simultaneous estimation of both system and input possesses too many degrees of freedom to guarantee uniqueness); but we do, nonetheless, identify system parameters and input excitation characteristics that are physically plausible and intuitively reasonable, without making input excitation assumptions. Simulated and laboratory experimental data are used to verify the algorithm and demonstrate its advantages over conventional approaches.     

Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap

This paper presents a bootstrap approach for integration of parametric and probabilistic cost estimation techniques. In the proposed method, a combination of regression analysis and bootstrap resampling technique is used to develop range estimates for construction costs. The method is applied to parametric range estimation of building projects as an example. The bootstrap approach includes advantages of probabilistic and parametric estimation methods, at the same time it requires fewer assumptions compared to classical statistical techniques. This study is of relevance to practitioners and researchers, as it provides a robust method for conceptual estimation of construction costs.     

Identification of Structural Systems Using an Evolutionary Strategy

The problem of system identification is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms that use measured data from the system to characterize it without significant a priori knowledge of the system. In this paper, a parameter estimation technique based on an evolution strategy (an optimization algorithm inspired by natural evolution) is presented to overcome some of the difficulties encountered in the field. Using this method, a set of direct problems is solved instead of directly tackling the inverse problem. If the uniqueness of the identification solution is guaranteed for the assumed model and the available data, this heuristic method is able to find a solution without incurring restrictions of other classical optimization methods, like the need for reliable initial estimates and convergence to local optima. Some results obtained with this algorithm are presented for the identification of 3 degrees of freedom (DOF) and a 10?DOF structural system under conditions including limited input/output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness of the system.     

Estimation of component and parameter distributions in spectral analysis

A method is presented for estimating the distributions of the components and parameters determined with spectral analysis when it is applied to a single data set. The method uses bootstrap resampling to simulate the effect of noise on the computed spectrum and to correct for possible bias in the estimates. A number of bootstrap procedures are reviewed, and one is selected for application to the kinetic analysis of positron emission tomography dynamic studies. The technique is shown to require minimal assumptions about noise in the measurements, and its small sample properties are established through Monte-Carlo simulations. The advantages and limitations of spectral analysis with bootstrap resampling for deriving inferences for tracer kinetic modeling are illustrated through sample analyses of time-activity curves for [18F]fluorodeoxyglucose and [15O]-labeled water.     

Use of GA to Determine Areas of Accretionto Semiconfined Aquifer

The goal of any groundwater inverse problem is to identify the distribution of an input function or certain other variables describing the unique flow dynamics of an aquifer system. A genetic algorithm (GA) combined with a numerical modeling technique is useful in determining both the spatial distribution and the flux represented by the accretion component of the groundwater flow equation. The GA technique was compared to a modified Gauss-Newton iterative technique. Binary and hexadecimal representations provided mapping of parameters from genetic operators to the numerical model. The technique used the patterns that developed in the string representations to determine probability regions. Two synthetic test cases were used to test the effectiveness of the technique. The stability of the technique was evaluated by introducing random error into the observation data. The technique was capable of locating the accretion area and tended to converge to a flux most representative of the flux entering the aquifer. However, the technique was susceptible to typical problems affecting the inverse problem, such as nonuniqueness.     

Using derivative estimates to describe intraindividual variability at multiple time scales.

The study of intraindividual variability is central to the study of individuals in psychology. Previous research has related the variance observed in repeated measurements (time series) of individuals to traitlike measures that are logically related. Intraindividual measures, such as intraindividual standard deviation or the coefficient of variation, are likely to be incomplete representations of intraindividual variability. This article shows that the study of intraindividual variability can be made more productive by examining variability of interest at specific time scales, rather than considering the variability of entire time series. Furthermore, examination of variance in observed scores may not be sufficient, because these neglect the time scale dependent relationships between observations. The current article outlines a method of using estimated derivatives to examine intraindividual variability through estimates of the variance and other distributional properties at multiple time scales. In doing so, this article encourages more nuanced discussion about intraindividual variability and highlights that variability and variance are not equivalent. An example with simulated data and an example relating variability in daily measures of negative affect to neuroticism are provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Single injection thermodilution. A flow-corrected method

BACKGROUND: Application of the Stewart-Hamilton equation in the thermodilution technique requires flow to be constant. In patients in whom ventilation of the lungs is controlled, flow modulations may occur leading to large errors in the estimation of mean cardiac output. METHODS: To eliminate these errors, a modified equation was developed. The resulting flow-corrected equation needs an additional measure of the relative changes of blood flow during the period of the dilution curve. Relative flow was computed from the pulmonary artery pressure with use of the pulse contour method. Measurements were obtained in 16 patients undergoing elective coronary artery bypass surgery. In 11 patients (group A), pulmonary artery pressure was measured with a catheter tip transducer, in a partially overlapping group of 11 patients (group B), it was measured with a fluid-filled system. For reference cardiac output we used the proven method of four uncorrected thermodilution estimates equally spread over the ventilatory cycle. RESULTS: A total of 208 cardiac output estimates was obtained in group A, and 228 in group B. In group B, 48 estimates could not be corrected because of insufficient pulmonary artery pressure waveform quality from the fluid-filled system. Individual uncorrected Stewart-Hamilton estimates showed a large variability with respect to their mean. In group A, mean cardiac output was 5.01 l/min with a standard deviation of 0.53 l/min, or 10.6%. After flow correction, this scatter decreased to 5.0% (P < 0.0001). With no bias, the corresponding limits of agreement decreased from +/- 1.06 to +/- 0.5 l/min after flow correction. In group B, the scatter decreased similarly and the limits of agreement also became +/- 0.5 l/min after flow correction. CONCLUSION: It was concluded that a single thermodilution cardiac output estimate using the flow-corrected equation is clinically feasible. This is obtained at the cost of a more complex computation and an extra pressure measurement, which often is already available. With this technique it is possible to reduce the fluid load to the patient considerably.     

Bootstrap confidence intervals: Good or bad?

The bootstrap is a nonparametric technique for estimating standard errors and approximate confidence intervals. Rasmussen has used a simulation experiment to suggest that bootstrap confidence intervals perform very poorly in the estimation of a correlation coefficient. Part of Rasmussen's simulation is repeated. A careful look at the results shows the bootstrap intervals performing quite well. Some remarks are made concerning the virtues and defects of bootstrap intervals in general. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Approximate standard errors and confidence intervals for indices of positive and negative agreement

Indices of positive and negative agreement for observer reliability studies, in which neither observer can be regarded as the standard, have been proposed. In this article, it is demonstrated by means of an example and a small simulation study that a recently published method for constructing confidence intervals for these indices leads to intervals that are too wide. Appropriate asymptotic (i.e., large sample) variance estimates and confidence intervals for the positive and negative agreement indices are presented and compared with bootstrap confidence intervals. We also discuss an alternative method of interval estimation motivated from a Bayesian viewpoint. The asymptotic intervals performed adequately for sample sizes of 200 or more. For smaller samples, alternative confidence intervals such as bootstrap intervals or Bayesian intervals should be considered.     

Validation of new pulsed Doppler echocardiographic techniques for assessment of pulmonary hemodynamics

In preparation for a vasodilator study on chronic obstructive pulmonary disease (COPD), we investigated the reliability of recently described pulsed Doppler techniques for estimating pulmonary artery pressure (PAP) and cardiac output (CO). Our aims were to determine the following: (1) the imaging success rate for pulsed Doppler measurements; (2) the repeatability of the measurements, and interobserver and intraobserver variability; and (3) the accuracy of Doppler compared with catheter measurements. Doppler studies were attempted in 81 patients (cardiac disease [23], COPD [22], sleep apnea [32], and normal subjects [4]). Suitable images were obtained in 68 subjects (84 percent) and in 76 subjects (94 percent) for PAP and CO estimations, respectively. The lowest imaging success rates were in COPD patients (68 percent for PAP and 86 percent for CO estimation). Repeatability of the techniques was assessed in four cardiac patients and three healthy volunteers by performing four replicate studies in each subject over 1 h. Intrasubject coefficient of variation was < 10 percent for PAP and < 5 percent for CO. The intraobserver variability for Doppler estimation of systolic and mean PAP was 5.5 percent and 5.8 percent, respectively. The corresponding values for interobserver variability were 6.7 percent and 6.2 percent. Intraobserver and interobserver variability for "nongeometric" method of estimating CO was 5.1 percent and 5.9 percent, respectively. Agreement was good between catheter-measured and Doppler-estimated PAP in the 27 patients tested (cardiac [19] and COPD [8]) for both mean and systolic pressures (r = 0.96 and r = 0.97, respectively). The correlations between thermodilution and Doppler estimations of CO in eight COPD patients were 0.77 ("geometric" technique) and 0.97 ("nongeometric" technique). We conclude that pulsed Doppler techniques can be used to obtain accurate and reproducible quantitative information on pulmonary hemodynamics in a wide range of patients. Suitable Doppler images can be obtained in more than two thirds of COPD patients.     

Effects of auditory stimulation and intelligence on time estimation in delinquents and nondelinquents.

Estimates of brief time intervals—ranging from 2-120 sec.—were obtained from 24 young offenders and 48 controls by the methods of production and verbal estimation. The verbal estimations were obtained of "empty" intervals as well as of intervals "filled" with a buzzer tone. Intelligence estimates were obtained on all Ss. The results indicate that brief time intervals appear longer to delinquents than to nondelinquents. The controls, but not the delinquents, gave shorter verbal estimates of the "filled" than of the "empty" intervals. Intelligence was not a significant source of variance in either the delinquents' or the controls' verbal estimation scores, but correlated significantly with the delinquents' production scores of the relatively longer intervals (15-120 sec.). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Spatio-temporal EEG source localization using simulated annealing

The estimation of multiple dipole parameters in spatio-temporal source modeling (STSM) of electroencephalographic (EEG) data is a difficult nonlinear optimization problem due to multiple local minima in the cost function. A straightforward iterative optimization approach to such a problem is very susceptible to being trapped in a local minimum, thereby resulting in incorrect estimates of the dipole parameters. In this paper, we present and evaluate a more robust optimization approach based on the simulated annealing algorithm. The complexity of this approach for the STSM problem was reduced by separating the dipole parameters into linear (moment) and nonlinear (location) components. The effectiveness of the proposed method and its superiority over the traditional nonlinear simplex technique in escaping local minima were tested and demonstrated through computer simulations. The annealing algorithm and its implementation for multidipole estimation are also discussed. We found the simulated annealing approach to be 7-31% more effective than the simplex method at converging to the true global minimum for a number of different kinds of three-dipole problems simulated in this work. In addition, the computational cost of the proposed approach was only marginally higher than its simplex counterpart. The annealing method also yielded similar solutions irrespective of the initial guesses used. The proposed simulated annealing method is an attractive alternative to the simplex method that is currently more common in dipole estimation applications.     

确定性多变量自校正控制的稳定性、收敛性和鲁棒性

本文用基于传递函数概念的虚拟等价系统方法统一分析各种类型的多变量确定性自校正控制系统的稳定性、收敛性和鲁棒性,分别针对参数估计收敛到真值、参数估计收敛到非真值以及参数估计不收敛的3种情况给出若干定理、推论和注释.在各个判据的基础上,进一步深化对确定性多变量自校正控制系统的理解.所得结论说明:参数估计的收敛性不是确定性多变量自校正控制系统稳定和收敛的必要条件;系统自身的反馈信息对确定性多变量自校正控制是充分的,即外加激励信号不是必要的.      

Price elasticities of demand for curative health care with control for sample selectivity on endogenous illness: an analysis for Sri Lanka

Estimation of demand for health care with samples of only the ill may bias estimates. Additionally, the lack of exogenous information, especially distance, about the alternative care providers causes omitted variable problems. This paper alleviates both problems through geographic mapping of facility information to individuals, combined with joint estimation of illness (health production) and health care demand. The joint estimation full sample demand results are compared to those from one equation estimation for only the ill sample. The results indicate that the selectivity problem is significant, but that for this sample the magnitude of the bias on the price coefficient is small.     

Bootstrap estimates of standard errors in validity generalization.

Bootstrapping is introduced as a method for approximating the standard errors of validity generalization (VG) estimates. A Monte Carlo study was conducted to evaluate the accuracy of bootstrap validity-distribution parameter estimates, bootstrap standard error estimates, and nonparametric bootstrap confidence intervals. In the simulation study the authors manipulated the sample sizes per correlation coefficient, the number of coefficients per VG analysis, and the variance of the distribution of true correlation coefficients. The results indicate that the standard error estimates produced by the bootstrapping procedure were very accurate. It is recommended that the bootstrap standard-error estimates and confidence intervals be used in the interpretation of the results of VG analyses. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Complex and frequency-dependent compliance of viscoelastic windkessel resolves contradictions in elastic windkessels

Based on simulated data, recent studies by others showed that fitting measured pulse pressure with the pulse pressure predicted by the two-element windkessel (W2-based pulse pressure method, PPM) yielded estimates of total arterial compliance closer to simulated values than other estimation methods that use either the W2 model or the three-element windkessel (W3). A later experimental application of the PPM, made by us, however, yielded relatively non pressure dependent estimates of compliance that were in contradiction with pressure dependent estimates obtained from the W2 model by fitting to the full aortic pressure wave (full pressure method, FPM). To explain these contradictory findings, in the present study we interpreted the aortic input impedance in terms of a viscoelastic windkessel (VW), where total peripheral resistance is connected in parallel to a complex and frequency dependent compliance, Cc(j omega), described by the Voigt cell. Using ascending aortic pressure and flow taken from four dogs, under a variety of haemodynamic states, we compared the estimates of compliance obtained from the W3 and VW models and from different W2-based estimation methods: the FPM (Cw2), the PPM (Cpp), the decay time method, DTM (Cdt), and the area method, AM (C(am)). The VW-based estimates of complex compliance resolved contradictions in the W2-based estimates. Static compliance of VW-model, Cvw = Cc(0), showed a good correlation (p = 0.999) with Cw2. Correlation of static compliance with C(am) and Cdt estimates was affected by distortions in diastolic pressure decay. The modulus of VW model's dynamic compliance, ?Cc(omega(h))?, at the heart pulsation omega(h), was well correlated (p = 0.975) with Cpp. Analysis of data fit and compliance estimates indicated that the VW model yields an improvement over the W3 in the physical interpretation of the overall arterial properties.     

Correcting for measurement error in the analysis of case-control data with repeated measurements of exposure

The authors present a technique for correcting for exposure measurement error in the analysis of case-control data when subjects have a variable number of repeated measurements, and the average is used as the subject's measure of exposure. The true exposure as well as the measurement error are assumed to be normally distributed. The method transforms each subject's observed average by a factor which is a function of the measurement error parameters, prior to fitting the logistic regression model. The resulting logistic regression coefficient estimate based on the transformed average is corrected for error. A bootstrap method for obtaining confidence intervals for the true regression coefficient, which takes into account the variability due to estimation of the measurement error parameters, is also described. The method is applied to data from a nested case-control study of hormones and breast cancer.     

Bootstrapping for pharmacokinetic models: visualization of predictive and parameter uncertainty

PURPOSE: We explore use of "bootstrapping" methods to obtain a measure of reliability of predictions made in part from fits of individual drug level data with a pharmacokinetic (PK) model, and to help clarify parameter identifiability for such models. METHODS: Simulation studies use four sets (A-D) of drug concentration data obtained following a single oral dose. Each set is fit with a two compartment PK model, and the "bootstrap" is employed to examine the potential predictive variation in estimates of parameter sets. This yields an empirical distribution of plausible steady state (SS) drug concentration predictions that can be used to form a confidence interval for a prediction. RESULTS: A distinct, narrow confidence region in parameter space is identified for subjects A and B. The bootstrapped sets have a relatively large coefficient of variation (CV) (35-90% for A), yet the corresponding SS drug levels are tightly clustered (CVs only 2-9%). The results for C and D are dramatically different. The CVs for both the parameters and predicted drug levels are larger by a factor of 5 and more. The results reveal that the original data for C and D, but not A and B, can be represented by at least two different PK model manifestations, yet only one provides reliable predictions. CONCLUSIONS: The insights gained can facilitate making decisions about parameter identifiability. In particular, the results for C and D have important implications for the degree of implicit overparameterization that may exist in the PK model. In cases where the data support only a single model manifestation, the "bootstrap" method provides information needed to form a confidence interval for a prediction.     

The identification of nonlinear biological systems: Volterra kernel approaches

Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.