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At least 19 recordsLinked to original sources

PubMed2006

Fast parametric imaging algorithm for dual-input biomedical system parameter estimation.

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.

Algorithms↗

PubMed2003

Parameter estimation in modeling phosphocreatine recovery in human skeletal muscle.

Commonly, muscle phosphocreatine (PCr) recovery from exercise has been fitted to a monoexponential function. However, a number of experiments indicate that at low muscle pH, a monoexponential fit is not suitable. We have performed in vivo 31P-MRS measurements of PCr during recovery from high-intensity intermittent exercise where muscle pH dropped below 6.5 (35 out of 40 cases). Results of a statistical analysis showed that monoexponentiality should be rejected in 32 out of 40 cases. Therefore, a Monte Carlo simulation was carried out to test the quality of competing models used in the literature at low pH: monoexponential, biexponential and changing rate utilization resource (CRUR). For each model, random Gaussian-distributed errors were imposed on simulated PCr recovery before performing the fits. A monoexponential function (three estimated parameters) provided a correct estimation of parameters (unbiased, normally distributed, poorly correlated estimates) and, therefore, should be preferred. When alternative functions are required, as in 32 cases out of 40 in the present study, it is demonstrated that a biexponential function (five estimated parameters) is not well suited (estimates were correlated), whereas a CRUR function (four estimated parameters) provides correct estimation of the parameters. It is concluded that a biexponential fit to PCr recovery is too sensitive to experimental errors to be practicable. Statistical and physiological relevance of CRUR are discussed.

Adaptation, Physiological↗

PubMed2002

Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability.

Three methods for fitting the diffusion model (Ratcliff, 1978) to experimental data are examined. Sets of simulated data were generated with known parameter values, and from fits of the model, we found that the maximum likelihood method was better than the chi-square and weighted least squares methods by criteria of bias in the parameters relative to the parameter values used to generate the data and standard deviations in the parameter estimates. The standard deviations in the parameter values can be used as measures of the variability in parameter estimates from fits to experimental data. We introduced contaminant reaction times and variability into the other components of processing besides the decision process and found that the maximum likelihood and chi-square methods failed, sometimes dramatically. But the weighted least squares method was robust to these two factors. We then present results from modifications of the maximum likelihood and chi-square methods, in which these factors are explicitly modeled, and show that the parameter values of the diffusion model are recovered well. We argue that explicit modeling is an important method for addressing contaminants and variability in nondecision processes and that it can be applied in any theoretical approach to modeling reaction time.

Humans↗

PubMed2002

HLODs, trait models, and ascertainment: implications of admixture for parameter estimation and linkage detection.

Maximizing the homogeneity lod is known to be an appropriate procedure for estimating parameters of the trait model in an approximately 'ascertainment assumption free' (AAF) manner. We have investigated whether this same property also holds for the heterogeneity lod (HLOD). We show that, when the genetic models at linked and unlinked loci differ, HLODs are not AAF, and maximizing the HLOD yields parameter estimates that are for all practical purposes meaningless; indeed, the admixture parameter alpha does not even measure the proportion of linked families within the sample, as is commonly supposed. In spite of this, our results confirm a large body of evidence supporting the use of HLODs as robust tools for linkage detection, and suggest further that maximizing the HLOD over both alpha and parameters of the trait model can improve accuracy in estimation of the recombination fraction theta;. These findings have important implications for the optimal handling of nuisance parameters in linkage analysis, particularly when evaluating the evidence for or against linkage based on multiple independent heterogeneous sets of data.

Animals↗

PubMed1991

A simulation model of AIDS in San Francisco: I. Model formulation and parameter estimation.

A model is formulated for the spread of the human immunodeficiency virus (HIV) and the subsequent development of acquired immunodeficiency syndrome (AIDS) in the population of homosexual men in San Francisco. The dynamic simulation model includes sexually very active and active subpopulations, migration, and a staged progression of HIV-infected persons to AIDS and death. Numerous data sources are used to estimate parameter values in the model. In a companion paper, simulations using the model and parameter estimates are found that are consistent with HIV and AIDS incidence data.

Acquired Immunodeficiency Syndrome↗

PubMed1996

Parameter estimation from incomplete data in binomial regression when the missing data mechanism is nonignorable.

We propose a method for estimating parameters in binomial regression models when the response variable is missing and the missing data mechanism is nonignorable. We assume throughout that the covariates are fully observed. Using a logit model for the missing data mechanism, we show how parameter estimation can be accomplished using the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). An example from the Six Cities Study (Ware et al., 1984, American Review of Respiratory Diseases 129, 366-374) is presented to illustrate the method.

Air Pollution↗

PubMed1998

A new method to estimate parameters of linear compartmental models using artificial neural networks.

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.

Algorithms↗

PubMedDate not supplied

Parameter estimation methods for single neuron models.

With the advancement in computer technology, it has become possible to fit complex models to neuronal data. In this work, we test how two methods can estimate parameters of simple neuron models (passive soma) to more complex ones (neuron with one dendritic cylinder and two active conductances). The first method uses classical voltage traces resulting from current pulses injection (time domain), while the second uses measures of the neuron's response to sinusoidal stimuli (frequency domain). Both methods estimate correctly the parameters in all cases studied. However, the time-domain method is slower and more prone to estimation errors in the cable parameters than the frequency-domain method. Because with noisy data the goodness of fit does not distinguish between different solutions, we suggest that running the estimation procedure a large number of times might help find a good solution and can provide information about the interactions between parameters. Also, because the formulation used for the model's response in the frequency domain is analytical, one can derive a local sensitivity analysis for each parameter. This analysis indicates how well a parameter is likely to be estimated and helps choose an optimal stimulation protocol. Finally, the tests suggest a strategy for fitting single-cell models using the two methods examined.

Animals↗

PubMed2003

Rasch fit statistics as a test of the invariance of item parameter estimates.

The invariance of the estimated parameters across variation in the incidental parameters of a sample is one of the most important properties of Rasch measurement models. This is the property that allows the equating of test forms and the use of computer adaptive testing. It necessarily follows that in Rasch models if the data fit the model, than the estimation of the parameter of interest must be invariant across sub-samples of the items or persons. This study investigates the degree to which the INFIT and OUTFIT item fit statistics in WINSTEPS detect violations of the invariance property of Rasch measurement models. The test in this study is a 80 item multiple-choice test used to assess mathematics competency. The WINSTEPS analysis of the dichotomous results, based on a sample of 2000 from a very large number of students who took the exam, indicated that only 7 of the 80 items misfit using the 1.3 mean square criteria advocated by Linacre and Wright. Subsequent calibration of separate samples of 1,000 students from the upper and lower third of the person raw score distribution, followed by a t-test comparison of the item calibrations, indicated that the item difficulties for 60 of the 80 items were more than 2 standard errors apart. The separate calibration t-values ranged from +21.00 to -7.00 with the t-test value of 41 of the 80 comparisons either larger than +5 or smaller than -5. Clearly these data do not exhibit the invariance of the item parameters expected if the data fit the model. Yet the INFIT and OUTFIT mean squares are completely insensitive to the lack of invariance in the item parameters. If the OUTFIT ZSTD from WINSTEPS was used with a critical value of | t | > 2.0, then 56 of the 60 items identified by the separate calibration t-test would be identified as misfitting. A fourth measure of misfit, the between ability-group item fit statistic identified 69 items as misfitting when a critical value of t > 2.0 was used. Clearly relying solely on the INFIT and OUTFIT mean squares in WINSETPS to assess the fit of the data to the model would cause one to miss one of the most important threats to the usefulness of the measurement model.

Calibration↗

PubMed2004

T-S model based indirect adaptive fuzzy control using online parameter estimation.

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation.

Algorithms↗

PubMed2000

Parameter estimation in a Gompertzian stochastic model for tumor growth.

The problem of estimating parameters in the drift coefficient when a diffusion process is observed continuously requires some specific assumptions. In this paper, we consider a stochastic version of the Gompertzian model that describes in vivo tumor growth and its sensitivity to treatment with antiangiogenic drugs. An explicit likelihood function is obtained, and we discuss some properties of the maximum likelihood estimator for the intrinsic growth rate of the stochastic Gompertzian model. Furthermore, we show some simulation results on the behavior of the corresponding discrete estimator. Finally, an application is given to illustrate the estimate of the model parameters using real data.

Angiogenesis Inhibitors↗

PubMed2004

Constructing sequence alignments from a Markov decision model with estimated parameter values.

Current methods for aligning biological sequences are based on dynamic programming algorithms. If large numbers of sequences or a number of long sequences are to be aligned, the required computations are expensive in memory and central processing unit (CPU) time. In an attempt to bring the tools of large-scale linear programming (LP) methods to bear on this problem, we formulate the alignment process as a controlled Markov chain and construct a suggested alignment based on policies that minimise the expected total cost of the alignment. We discuss the LP associated with the total expected discounted cost and show the results of a solution of the problem based on a primal-dual interior point method. Model parameters, estimated from aligned sequences, along with cost function parameters are used to construct the objective and constraint conditions of the LP problem. This article concludes with a discussion of some alignments obtained from the LP solutions of problems with various cost function parameter values.

Algorithms↗

PubMed2000

Statistical analysis of nonlinear parameter estimation for Monod biodegradation kinetics using bivariate data.

A nonlinear regression technique for estimating the Monod parameters describing biodegradation kinetics is presented and analyzed. Two model data sets were taken from a study of aerobic biodegradation of the polycyclic aromatic hydrocarbons (PAHs), naphthalene and 2-methylnaphthalene, as the growth-limiting substrates, where substrate and biomass concentrations were measured with time. For each PAH, the parameters estimated were: q(max), the maximum substrate utilization rate per unit biomass; K(S), the half-saturation coefficient; and Y, the stoichiometric yield coefficient. Estimating parameters when measurements have been made for two variables with different error structures requires a technique more rigorous than least squares regression. An optimization function is derived from the maximumlikelihood equation assuming an unknown, nondiagonal covariance matrix for the measured variables. Because the derivation is based on an assumption of normally distributed errors in the observations, the error structures of the regression variables were examined. Through residual analysis, the errors in the substrate concentration data were found to be distributed log-normally, demonstrating a need for log transformation of this variable. The covariance between ln C and X was found to be small but significantly nonzero at the 67% confidence level for NPH and at the 94% confidence level for 2MN. The nonlinear parameter estimation yielded unique values for q(max), K(S), and Y for naphthalene. Thus, despite the low concentrations of this sparingly soluble compound, the data contained sufficient information for parameter estimation. For 2-methylnaphthalene, the values of q(max) and K(S) could not be estimated uniquely; however, q(max)/K(S) was estimated. To assess the value of including the relatively imprecise biomass concentration data, the results from the bivariate method were compared with a univariate method using only the substrate concentration data. The results demonstrated that the bivariate data yielded a better confidence in the estimates and provided additional information about the model fit and model adequacy. The combination of the value of the bivariate data set and their nonzero covariance justifies the need for maximum likelihood estimation over the simpler nonlinear least squares regression.

Biodegradation, Environmental↗

PubMed1997

Genetic parameter estimates for growth and fleece characteristics in Targhee sheep.

Weaning weights at 60 (WW60) and 120 d (WW120), 60- to 120-d postweaning gains (PWG) for lambs weaned at 60 d, 120- to 365-d postweaning gains (YG) for lambs weaned at 120 d, fleece weights (FWT), and fiber diameters (FD) from 20 Targhee flocks were used to estimate parameters required for multiple-trait genetic evaluation. Flocks from western states (n = 10) recorded primarily WW60 (n = 1,762), WW120 (n = 5,961), YG (n = 2,388), FWT (n = 2,824), and FD (n = 2,000). Eastern flocks primarily recorded WW60 (n = 1,754) and PWG (n = 1,237). Heritability estimates were .01 for WW60 (.00 for western flocks and .07 for eastern flocks), .10 for WW120, .33 for PWG, .20 for YG, .41 for FWT, and .58 for FD. Additive maternal and maternal permanent environmental effects as a proportion of phenotypic variance were .10 and .09, respectively, for WW60 and .05 and .08 for WW120. In western flocks, maternal additive and permanent environmental effects on WW60 and WW120 were highly correlated (> .81), whereas WW120 and YG had a small positive additive genetic correlation (.19) but a negative residual correlation (-.34). Fleece weight had a genetic correlation of .50 with WW120 and YG. Supplemental analyses suggested that the observed genetic relationship between fleece weight and weaning weight arose primarily from a genetic association between additive direct genetic effects on fleece weight and additive maternal effects on weaning weight. Fiber diameter was nearly independent of body weights but had an undesirable additive correlation of .51 with FWT. In eastern flocks, WW60 and PWG had an additive correlation of .71 and a residual correlation of .15.

Aging↗

PubMed2003

Parameter estimation in a model for multidimensional recording of neuronal data: a Gibbsian approximation approach.

This article proposes improved numerical procedures for estimating parameters in a spatiotemporal lattice model introduced for the analysis of cortical activities monitored from arrays of diodes. The numerical algorithms are based on approximations inspired by statistical physics. Both Gibbsian and mean-field approximations are used; they allow for computing local conditional probabilities inside the lattice. The statistical procedures rely on the computation of pseudomaximum-likelihood estimators. The estimators are evaluated on the basis of Monte Carlo simulations. These simulations show that mean-field approximations are useful for reducing the variance of estimators when the data are recorded from arrays of 144 diodes (which are in accordance with standard practice). In light of these improved methods, we give new interpretations for a data set obtained from optical recording of a Guinea pig's auditory cortex in response to pure tone stimulations.

Algorithms↗

PubMed2002

The automatic lung parameter estimator (ALPE) system: non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes.

OBJECTIVE: Clinical measurements of pulmonary gas exchange abnormalities might help prevent hypoxaemia and be useful in monitoring the effects of therapy. In clinical practice single parameters are often used to describe the abnormality e.g., the "effective shunt." A single parameter description is often insufficient, lumping the effects of several abnormalities. A more detailed picture can be obtained from experiments where FiO2 is varied and two parameters estimated. These experiments have previously taken 30-40 minutes to complete, making them inappropriate for routine clinical use. However with automation of data collection and parameter estimation, the experimental time can be reduced to 10-15 minutes. METHODS: A system has been built for non-invasive, Automatic, Lung Parameter Estimation (ALPE). This system consists of a ventilator, a gas analyser with pulse oximeter, and a computer. Computer programs control the experimental procedure, collect data from the ventilator and gas analyser, and estimate pulmonary gas exchange parameters. Use of the ALPE system, i.e. in estimating gas exchange parameters and reducing experimental time, has been tested on five normal subjects, two patients before and during diuretic therapy, and on 50 occasions in patients before and after surgical intervention. RESULTS: The ALPE system provides estimation of pulmonary gas exchange parameters from a simple, clinical, non-invasive procedure, automatically and quickly. For normal subjects and in patients receiving diuretic therapy, data collection by clinicians familiar with ALPE took (mean +/- SD) 13 min 40 sec +/- 1 min 23 sec. For studies on patients before and after surgery, data collection by an intensive care nurse took (mean +/- SD) 10 min 47 sec +/- 2 min 14 sec. Parameter estimates were: for normal subjects, shunt = 4.95% +/- 2.64% and fA2 = 0.89 +/- 0.01; for patients with heart failure prior to diuretic therapy, patient 1, shunt = 11.50% fA2 = 0.41, patient 2 shunt = 11.61% fA2 = 0.55; and during therapy: patient 1, shunt = 11.51% fA2 = 0.71, patient 2, shunt = 11.22% fA2 = 0.49. CONCLUSIONS: The ALPE system provides quick, non-invasive estimation of pulmonary gas exchange parameters and may have several clinical applications. These include, monitoring pulmonary gas exchange abnormalities in the ICU, assessing post-operative gas exchange abnormalities, and titrating diuretic therapy in patients with heart failure.

Diuretics↗

PubMed1997

Application of genetic algorithms to parameter estimation of bioprocesses.

The paper explains the application of a genetic algorithm (GA) to the problem of estimating parameters for a kinetic model of a biologically reacting system. It is demonstrated that the GA is a powerful tool for quantifying the kinetic parameters using kinetic data. As the operation of the GA does not depend on the form of the model equation, it can be applied to the wide spectrum of kinetic modelling problems without any complex formulation procedure.

Algorithms↗

PubMed2005

Automated estimator parameter selection for an IBM head/disk assembly.

This paper presents the application of a discrete adaptive observer (DAO) to an IBM head/disk assembly system. Because of the difficulties in tuning, a genetic algorithm is implemented off-line to obtain optimal observer parameters for the DAO. Simulations show that the genetic algorithm is successful in choosing appropriate observer gains. Furthermore, as a result of these optimal gains, the observer state and parameter estimates converge accurately and quickly.

Journal Article↗