Yields Parameter Estimates Research Articles

Delayed hemodynamic responses in schizophrenia

While there are many reports of reduced amplitude of hemodynamic responses in schizophrenia, there are no reports of delayed hemodynamic responses, in spite of event-related brain potential (ERP) evidence of slowed neural responses. Recently, Henson et al. (2002) [Henson, R., Price, C., Rugg, M., Turner, R., Friston, K., 2002. Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations. NeuroImage, 15:83–97] proposed a new method for testing small latency effects (<2 s) in hemodynamic responses. fMRI data were collected during a visual oddball task with infrequent (12%) targets (K) and frequent (88%) standards (X), presented every 1–3 s pseudorandomly, with 7–24 s between targets. SPM99 yielded parameter estimates for the hemodynamic response and its temporal derivative (TD). Beta images reflecting hemodynamic response magnitude to target stimuli were minimally thresholded ( P < 0.05, uncorrected), and latencies were estimated for surviving voxels using TD and hemodynamic response beta values. DSM-IV schizophrenia patients ( n = 12) and sex- and age-matched healthy control subjects ( n = 12) were recruited from the community. Although groups differed only minimally in activation height, hemodynamic responses were significantly delayed in basal ganglia, thalamus, and anterior cingulate in patients with schizophrenia. Psychomotor slowing reflected in reaction times to targets recorded outside the magnet was related to hemodynamic slowing in basal ganglia, anterior cingulate, thalamus, as well as left cerebellum in controls. Delays in the hemodynamic response have far-reaching implications for understanding the pathophysiology of schizophrenia from slowed neural responses to slowed substrate delivery, and depending of the degree of delay, may raise methodological issues regarding modeling hemodynamic responses in patients and controls.

System Identification of Nonlinear Mechanical Systems Using Embedded Sensitivity Functions

A novel method of experimental sensitivity analysis for nonlinear system identification of mechanical systems is examined here. It has been shown previously that embedded sensitivity functions, which are quadratic algebraic products of frequency response function data, can be used to identify structural design modifications for reducing vibration levels. It is shown here that embedded sensitivity functions can also be used to characterize and identify mechanical nonlinearities. Embedded sensitivity functions represent the rate of change of the response with variation in input amplitude, and yield estimates of system parameters without being explicitly dependent on them. Frequency response functions are measured at multiple input amplitudes and combined using embedded sensitivity analysis to extract spectral patterns for characterizing systems with stiffness and damping nonlinearities. By comparing embedded sensitivity functions with finite difference frequency response sensitivities, which incorporate the amplitude-dependent behavior of mechanical nonlinearities, models can be determined using an inverse problem that uses system sensitivity to estimate parameters. Expressions for estimating nonlinear parameters are derived using Taylor series expansions of frequency response functions in conjunction with the method of harmonic balance for periodic signals. Using both simulated and experimental data, this procedure is applied to estimate the nonlinear parameters of a two degree-of-freedom model and a vehicle exhaust system to verify the approach.

Relationship between temperature sensitivity of capillary pressure and soil particle size

In a review of data in the published literature, we found that the effect of temperature on capillary pressure in natural soils increased with decreasing geometric mean soil particle diameter. This observation had implications both for hydrologic modeling and for elucidating the mechanisms that controlled the temperature sensitivity of capillary pressure. Two mechanisms were suggested to explain this observation. The first mechanism proposed that changes in soil mineral particle size resulted in changes in the proportion of hydrophyllic and hydrophobic surfaces, yielding changes in their wettabilities, and temperature effects on capillary pressure. The second mechanism proposed that the observation was due to the effect of soil particle size on disjoining pressure. A model derived from the second mechanism could be fitted to the available data and yielded parameter estimates consistent with current models of disjoining pressure profiles above surfaces.

Clinical Efficacy and Pharmacokinetics of Montelukast in Dyspeptic Children with Duodenal Eosinophilia

ABSTRACTBackgroundMontelukast, a competitive cysteinyl leucotriene‐1 receptor antagonist, reduces airway eosinophilia in asthmatics. We evaluated the effect of this drug in children with eosinophilic duodenitis, defined histologically as duodenal mucosa with peak eosinophil count of more than 10 eosinophils/hpf.MethodsForty children and adolescents (6–18 yr) with dyspepsia and duodenal eosinophilia were enrolled in a double blind, randomized, placebo‐controlled, cross‐over study of monteleukast therapy. Subjects were randomized to receive either 10 mg montelukast or an identical placebo once daily and were evaluated on day 14 for symptomatic and biochemical responses. Subjects were also randomized to one of two blood sampling schemes to evaluate montelukast pharmacokinetics.ResultsUsing a post treatment global pain assessment, a positive clinical response was observed in 62.1% of patients receiving montelukast compared with 32.4% on placebo (p &lt; 0.02). Pain assessment score deteriorated in 45% of montelukast responders (5/11) after cross‐over to placebo and improved in 62% (8/13) of placebo non‐responders on cross‐over to montelukast. In patients with peak duodenal eosinophil counts between 20–29/hpf (n=19), a positive pain assessment response was observed in 84% of patients receiving montelukast compared to 42% receiving placebo (p &lt; 0.01). Response rate did not differ by age, gender or histologic findings at baseline. Pharmacokinetic analysis yielded parameter estimates for absorption rate constant (Ka), apparent volume of distribution (Vd/F) and elimination rate constant (Kel) of 0.42 h−1, 0.19 L/kg and 0.26 h−1, respectively. The relative extent of systemic drug exposure was comparable to that observed in previous pediatric investigations with similar weight‐adjusted montelukast doses. Neither dose nor calculated drug exposure were associated with the level of post treatment pain assessment or the change in biochemical markers.ConclusionsThese data suggest a beneficial role for montelukast in the treatment of pediatric patients with dyspepsia associated with duodenal eosinophilia.

Distribution of Rankings for Groups Exhibiting Heteroscedasticity and Correlation

In one-way analysis of variance, a main interest is in differences among the groups that comprise the population. For a given parameter, such as a mean value, data yield parameter estimates for each group, as well as group rankings based on these statistics. Here ranking probabilities are studied under the assumption that parameter estimates are well approximated by a normal distribution, either in finite samples or asymptotically, with possible intergroup heteroscedasticity and correlation. Particular interest lies in the ranking distribution as a descriptor of experiments on some future dataset. Examples include contract bidding, global economic competition, and rival contests in mating. Ranking distributions are analytically complicated, yet some interesting properties can be derived for them via the symmetry and elliptical geometry of the normal distribution. Some relationships between ranking probabilities and group parameters are described, with attention given to the role of between-group heterogeneity and correlation. For the ranking probabilities, estimators and asymptotically valid standard errors formulas and hypothesis tests are proposed. Simulation is used to describe the sample size needed for accurate asymptotic approximation, and the methods are illustrated with an economic example.

Multistage Functional Responses in a Ladybeetle-Aphid System: Scaling up from the Laboratory to the Field

Experiments and models are described that quantify the functional responses of the larval and adult stages of the sevenspot ladybeetle, Coccinella septempunctata L. toward mixed stage populations of the cotton aphid, Aphis gossypii Glover, in cotton. In the laboratory, functional responses were measured for five beetle stages and three size groups of prey at five temperatures (15 to 35°C). The 75 resulting functional responses were each characterized by a search rate (cm2/predator/d) and a handling rate (prey/predator/d). Both search and handling rates increased with predator stage. Search rates increased, while handling rates decreased, with prey size. Search rate increased linearly with temperature, while handling rate showed an optimum response to temperature. Field cage studies were conducted to verify whether the search rates determined in the laboratory were valid under field conditions. Search rates of predators derived from observations in field cages yielded parameter estimates that were similar to those found in the laboratory. A comprehensive model, allowing for effects of temperature, stage distribution of the aphid population, and plant leaf area on predation rate, was then constructed to calculate predation by larvae and adults of C. septempunctata on multi stage populations of prey. This model gave good correspondence to the cage observations if two-sided leaf area was input into the model as search substrate for the predator. The model appears suitable for calculating predation rates of C. septempunctata on A. gossypii under field conditions. A sensitivity analysis of the functional response model shows the crucial effect of crop leaf area on predation.

A structural model of aggregate US job flows: another look

AbstractA recent article (Collard et al., 2002) published in this journal presented a structural model of aggregate job flows. Unrestricted estimation of the model yields parameter estimates that would imply an unemployment rate of 99%. Instead of solving this problem by fixing one of the parameters, as originally attempted by the authors, I add moments regarding the employment rate. The new results call for a re‐evaluation of the model. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Assessing chronic disease progression using non-homogeneous exponential regression Markov models: an illustration using a selective breast cancer screening in Taiwan.

Previous research on estimation of the progression of chronic disease, from the normal preclinical screen-detectable phase (PCDP) to the final clinical phase, has usually assumed constant transition rates and has rarely addressed how relevant covariates affect multi-state transitions. The present study proposes two non-homogeneous models using the Weibull distribution and piecewise exponential model, together with covariate functions of the proportional hazard form, to tackle these problems. We illustrate the models by application to a selective breast cancer screening programme. The results of the Weibull model yield estimates of scale and shape parameters for annual preclinical incidence rate as 0.0000058 (SE=0.0000019) and 2.4755 (SE=0.1153), the latter being significantly higher than 1. Annual transition rate was estimated as 0.3153 (SE=0.1385). Relative risks for the effects of late age at first pregnancy (AP) and high body mass index (BMI) on preclinical incidence rate were 1.98 and 2.59, respectively. The corresponding figures on the transition from the PCDP to clinical phase were 1.56 and 1.99, respectively. Non-homogeneous Markov models proposed in this study can be easily applied to rates of progression of chronic disease with increasing or decreasing rates with time and to model the effect of relevant covariates on multi-state transition rates.

Sodium ion transport through gramicidin-doped bilayers. Influences of temperature and ionic concentration

The dependence on temperature and Na + concentration of ionic transport in lipid bilayers doped with the antibiotic gramicidin D (GD) were examined using electrochemical impedance spectroscopy (EIS). The experimental system consists of a lipid bilayer obtained from a mixture of phosphatidylcholine and dimethyldioctadecyl-ammonium chloride (DODAC) and supported on gold surfaces modified with a mercaptocarboxylic acid monolayer. This monolayer renders a negatively charged surface upon which spontaneous fusion of lipid vesicles forms the external bilayer attached by electrostatic interactions. A mathematical expression previously derived for the impedance of these membrane processes was used to characterize the system under different experimental conditions. Fitting results yield parameter estimates that confirm the dependence on cation concentration as predicted by the model. A linear behavior in an Arrhenius plot was observed between conductance and temperature.

LIVING ON THE EDGE OF CHAOS: POPULATION DYNAMICS OF FENNOSCANDIAN VOLES

Although ecologists were among the first scientists to study the implications of chaotic dynamics, there is still no widely accepted example of chaos in a field population. If current developments in nonlinear dynamics are to be of relevance to population ecology, however, it is necessary to determine whether chaos occurs in nature. Here we bring together two lines of evidence suggesting that the dynamics of vole populations in northern Fennoscandia may be chaotic: nonlinear analysis of time-series data, and a model based on biological mechanisms. We review how a mechanistic population model can be used to produce short-term population forecasts, even when one of the species is not observed, and then use this benchmark of model accuracy to support our choice of a mechanistic population model for vole dynamics. By adjusting model parameters to optimize the forecasting accuracy on the vole time series, we can obtain parameter estimates for the model. In tests on simulated data, this new approach to parameter estimation for stochastic dynamic models yields parameter estimates such that the fitted model produces dynamics qualitatively and quantitatively similar to those observed. However, some parameters are poorly identified (i.e., many different parameter combinations yield nearly identical model dynamics). For the best-fit parameters, the model dynamics are characterized by quasi-chaotic behavior; the global Lyapunov exponent is very close to (and statistically undistinguishable from) zero, but the median local Lyapunov exponent is significantly positive. Our findings of recurrent short-term chaos suggest that vole dynamics in northern Fennoscandia are characterized by a blend of order and irregularity. The irregularity is the same type as would occur in a noisy chaotic system (i.e., exogenous noise amplification by sensitive dependence on initial conditions) but only occurs during specific portions of the oscillation.

Robust regression for clustered data with application to binary responses.

Generalized estimating equations (GEE) can be highly influenced by the presence of unusual data points. A generalization of the GEE procedure, which yields parameter estimates and fitted values that are resistant to influential data, is introduced. Resistant generalized estimating equations (REGEE) include weights in the estimating equations to downweight influential observations or clusters. Influential observations are downweighted according to their leverage or residual in an example of correlated binary regression applied to 137 urinary incontinent elderly patients from 38 medical practices.

Laplace Importance Sampling for Generalized Linear Mixed Models

It is well known that the standard Laplace approximation of the integrated marginal likelihood function of a random effects model may be invalid if the dimension of the integral increases with the sample size and the resulting parameter estimates, especially those of the variance components, are biased towards zero. Bias-correction factors have been proposed in the literature but they are asymptotically correct only for the case of small variance components. Techniques for modifying the standard Laplace expansion have also been proposed but they are highly technical and problem-specific and hence unsuitable for routine use. Monte Carlo approximations of the marginal likelihood function typically make use of Markov Chain Monte Carlo (MCMC) sampling the convergence of which is difficult to check. We propose an importance sampling method where the the importance function is chosen with the aid of Laplace expansion. Since it is only used to suggest an appropriate importance function, the accuracy of Laplace expansion affects only the efficiency but not the unbiasedness of the resulting likelihood approximation. Moreover, the proposed method can be implemented using independent rather than MCMC sampling and so it is easier to check convergence and to assess variability. One set of simulations suffices for approximating the entire likelihood function but we do recommend iterating the importance sampling procedure a few times to obtain progressively better estimates of the likelihood function near its maximum. When applied to fit a logistic model with crossed random effects to the well known salamander mating data, the proposed method yields parameter estimates very comparable to those obtained using modified Laplace approximations. The proposed procedure also produces estimates very close to the “true” maximum likelihood estimates when applied to a set of binary data simulated from a crossed random effects model.

Modified Latin Hypercube Sampling Monte Carlo (MLHSMC) Estimation for Average Quality Index

The Monte Carlo (MC) method exhibits generality and insensitivity to the number of stochastic variables, but it is expensive for accurate Average Quality Index (AQI) or Parametric Yield estimation ...

Resistant fits for regression with correlated outcomes an estimating equations approach

The generalized estimating equations procedure of Liang and Zeger (1986) can be highly influenced by the presence of unusual data points. A generalization is introduced which yields parameter estimates and fitted values resistant to influential data. A diagonal weight matrix for each cluster is incorporated into the estimating equations which downweights the multivariate response vector element-wise. Efficiency of the procedure is investigated, including the case of correlated binary outcomes.

Estimation of the parameters of the K-distribution using higher order and fractional moments [radar clutter

We propose a method for estimating the parameters of the K-distribution in a manner which significantly reduces computational requirements when compared with existing approaches. Additionally, the method yields parameter estimates with lower variance when compared with standard techniques.

Cohort size and maximum likelihood estimation of mortality parameters.

The purpose of this study was to investigate the effects of cohort size on maximum likelihood estimates of mortality parameters. Recent experimental investigations have stressed the importance of large cohorts for detecting leveling off of mortality rates at older ages. In the present study, emphasis was placed on evaluation of relatively small cohorts (about 150-300 individuals). Deaths were simulated under the assumption of the frailty mortality model. Two different parameter sets that resulted in differences in mean life span of more than twofold were used for simulations. Our smallest cohorts yielded parameter estimates that had generally good statistical properties, but relatively large standard errors. For tests of hypotheses concerning equality of parameters among populations or experimental treatments, empirical standard errors (obtained from several cohorts) were preferable to asymptotic standard errors (obtained for single cohorts). In particular, empirical standard errors yielded reliable type I error rates.

A study of stratified sampling in variance reduction techniques for parametric yield estimation

The Monte Carlo (MC) method exhibits generality and insensitivity to the number of stochastic variables, but is expensive for accurate yield estimation of electronic circuits. In the literature, several variance reduction techniques have been described, e.g., stratified sampling. In this contribution the theoretical aspects of the partitioning scheme of the tolerance region in stratified sampling is presented. Furthermore, a theorem about the efficiency of this estimator over the primitive MC (PMC) estimator versus sample size is given. To the best of our knowledge, this problem was not previously studied in parametric yield estimation. In this method we suppose that the components of parameter disturbance space are independent or can be transformed to an independent basis. The application of this approach to a numerical example (Rosenbrock's curved-valley function) and a circuit example (Sallen-Key low-pass filter) are given.

Nonlinear Aspects of Goods-Market Arbitrage and Adjustment: Heckscher's Commodity Points Revisited

We propose that analysis of purchasing power parity (PPP) and the law of one price should explicitly take into account the possibility of “commodity points”—thresholds delineating a region of no central tendency among relative prices, possibly due to lack of perfect arbitrage in the presence of transaction costs and uncertainty. More than 80 years ago, Heckscher stressed the importance of such incomplete arbitrage in the empirical application of PPP. We devise an econometric method to identify commodity points. Price adjustment is treated as a nonlinear process, and a threshold autoregression offers a parsimonious specification within which both thresholds and adjustment speeds are estimated by maximum likelihood methods. Our model performs well using post-1980 data, and yields parameter estimates that appear quite reasonable: adjustment outside the thresholds might imply half-lives of price deviations measured in months rather than years, and the thresholds correspond to popular rough estimates as to the order of magnitude of actual transport costs. The estimated commodity points appear to be positively related to objective measures of market segmentation, notably nominal exchange rate volatility.J. Japan Int. Econ.December 1997,11(4), pp. 441–479. Department of Economics, University of California, Berkeley, California 94720-3880; and Department of Economics, Northwestern University, Evanston, Illinois 60208-2600.

Latin Hypercube Sampling Monte Carlo Estimation of AverageQuality Index for Integrated Circuits

The Monte Carlo method exhibits generality and insensitivity to the number of stochastic variables, but is expensive for accurate Average Quality Measure (AQI) or Parametric Yield estimation of MOS VLSI circuits. In this contribution a new method of variance reduction technique, viz. the Latin Hypercube Sampling (LHS) method is presented which improves the efficiency of AQI estimation in integrated circuits especially for MOS digital circuits. This method is similar to the Primitive Monte Carlo (PMC) method except in samples generation step where the Latin Hypercube Sampling method is used. This sampling method is very simple and does not involve any further simulations. Moreover, it has a smaller variance with respect to the PMC estimator. Encouraging results have thus far been obtained. A 3-dimensional quadratic function, a high pass filter, and a CMOS delay circuit examples are included to demonstrate the efficiency of this technique.

Optimal robot excitation and identification

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in the case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and acceleration in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.