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International audience

Term Structure and the Estimated Monetary Policy Rule in the Eurozone

Published as an article in: Spanish Economic Review, 2008, vol. 10, issue 4, pages 251-277.

The Variable Sample Size (X)over-bar Chart with Estimated Parameters

The VSS X chart, dedicated to the detection of small to moderate mean shifts in the process, has been investigated by several researchers under the assumption of known process parameters. In practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS chart when the process parameters are estimated, we compare them in the case where the process parameters are assumed known and we propose specific optimal control chart parameters taking the number of Phase I samples into account.

Steady state optimization of an ammonia reactor

A hybrid simulation technique for identification and steady state optimization of a tubular reactor used in ammonia synthesis is presented. The parameter identification program finds the catalyst activity factor and certain heat transfer coefficients that minimize the sum of squares of deviation from simulated and actual temperature measurements obtained from an operating plant. The optimization program finds the values of three flows to the reactor to maximize the ammonia yield using the estimated parameter values. Powell's direct method of optimization is used in both cases. The results obtained here are compared with the plant data.

Identification and optimization of ammonia reactors through hybrid simulation

A hybrid simulation technique for identification and steady state optimization of a tubular reactor used in ammonia synthesis is presented. The parameter identification program finds the catalyst activity factor and certain heat transfer coefficients that minimize the sum of squares of deviation from simulated and actual temperature measurements obtained from an operating plant. The optimization program finds the values of three flows to the reactor to maximize the ammonia yield using the estimated parameter values. Powell's direct method of optimization is used in both cases. The results obtained here are compared with the plant data.

Monte Carlo Valuation of natural gas investments

This paper deals with the valuation of energy assets related to natural gas. In particular, we evaluate a baseload Natural Gas Combined Cycle (NGCC) power plant and an ancillary instalation, namely a Liquefied Natural Gas (LNG) facility, in a realistic setting; specifically, these investments enjoy a long useful life but require some non-negligible time to build. Then we focus on the valuation of several investment options again in a realistic setting. These include the option to invest in the power plant when there is uncertainty concerning the initial outlay, or the option's time to maturity, or the cost of CO2 emission permits, or when there is a chance to double the plant size in the future. Our model comprises three sources of risk. We consider uncertain gas prices with regard to both the current level and the long-run equilibrium level; the current electricity price is also uncertain. They all are assumed to show mean reversion. The two-factor model for natural gas price is calibrated using data from NYMEX NG futures contracts. Also, we calibrate the one-factor model for electricity price using data from the Spanish wholesale electricity market, respectively. Then we use the estimated parameter values alongside actual physical parameters from a case study to value natural gas plants. Finally, the calibrated parameters are also used in a Monte Carlo simulation framework to evaluate several American-type options to invest in these energy assets. We accomplish this by following the least squares MC approach.

Comparison of two approaches for estimating natural mortality based on longevity

Vetter (1988) noted that her review of the estimation of the instantaneous natural mortality rate (M) was initiated by a discussion among colleagues that identified M as the single most impor ta nt but least well-estimated parameter in fishery models. A lthough much has been accomplished in the inter vening years, M remains one of the most difficult parameters to estimate in fishery stock assessments. A number of novel approaches using tagging and telemetry data provide promise for making reliable direct estimates of M for a given stock (Hearn et al., 1998 ; Frusher and Hoenig, 2001; Hightower et al., 2001; Latour et al., 2003; Pollock et al., 2004). However, such methods are often impracticable and fishery scientists must approximate M by using estimates made for other stocks of the same or similar species or by predicting M from features of the species’ life history (Beverton and Holt, 1959; Beverton, 1963; Alverson and Carney, 1975; Pauly, 1980; Hoenig, 1983; Peterson and Wroblewski, 1984; Roff, 1984; Gunderson and Dygert, 1988; Chen and Watanabe, 1989; Charnov, 1993; Jensen, 1996; Lorenzen, 1996).

Aplicação da técnica simulated annealing na investigação da ciclagem de nitrogênio na inteface água-sedimento

Neste trabalho é apresentado a aplicação de um método de otimização a fim de estimar parâmetros que normalmente estão presentes na modelagem matemática da dinâmica de espécies químicas na interface água-sedimento. O Problema Direto aqui consistiu na simulação das concentrações das espécies orgânicas e inorgânicas (amônia e nitrato) de nitrogênio, num ambiente idealizado, o qual foi fracionado em quatro camadas: uma camada de água (1 metro) e três camadas de sedimento (0-1 cm, 1-2 cm e 2-10 cm). O Problema Direto foi resolvido pelo Método de Runge Kutta, tendo sido gerada uma simulação de 50 dias. Na estimativa dos coeficientes de difusão e porosidade foi aplicado o Método Simulated Annealing (SA). A eficiência da estratégia aqui adotada foi avaliada através do confronto entre dados experimentais sintéticos e as concentrações calçadas pela solução do Problema Direto, adotando-se os parâmetros estimados pela SA. O melhor ajuste entre dados experimentais e valores calculados se deu quando o parâmetro estimado foi a porosidade. Com relação à minimização da função objetivo, a estimativa desse parâmetro também foi a que exigiu menor esforço computacional. Após a introdução de um ruído randômico às concentrações das espécies nitrogenadas, a técnica SA não foi capaz de obter uma estimativa satisfatória para o coeficiente de difusão, com exceção da camada 0-1 cm sedimentar. Para outras camadas, erros da ordem de 10 % foram encontrados (para amônia na coluna dágua, pro exemplo). Os resultados mostraram que a metodologia aqui adotada pode ser bastante promissora enquanto ferramenta de gestão de corpos dágua, especialmente daqueles submetidos a um regime de baixa energia, como lagos e lagoas costeiras.

Simulating Coxian Phase-type Distributions for Patient Survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.

Detection of Incipient Thermal Damage Of Carbon Fiber/Epoxy Composites Using Fluorescent Thermal Damage Probes

Thesis (Ph.D.)--University of Washington, 2013

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

On the observability of turbulent transport rates by Argo: supporting evidence from an inversion experiment

Although estimation of turbulent transport parameters using inverse methods is not new, there is little evaluation of the method in the literature. Here, it is shown that extended observation of the broad scale hydrography by Argo provides a path to improved estimates of regional turbulent transport rates. Results from a 20 year ocean state estimate produced with the ECCO v4 non-linear inverse modeling framework provide supporting evidence. Turbulent transport parameter maps are estimated under the constraints of fitting the extensive collection of Argo profiles collected through 2011. The adjusted parameters dramatically reduce misfits to in situ profiles as compared with earlier ECCO solutions. They also yield a clear reduction in the model drift away from observations over multi-century long simulations, both for assimilated variables (temperature and salinity) and independent variables (bio-geochemical tracers). Despite the minimal constraints imposed specifically on the estimated parameters, their geography is physically plausible and exhibits close connections with the upper ocean ocean stratification as observed by Argo. The estimated parameter adjustments furthermore have first order impacts on upper-ocean stratification and mixed layer depths over 20 years. These results identify the constraint of fitting Argo profiles as an effective observational basis for regional turbulent transport rates. Uncertainties and further improvements of the method are discussed.

A pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion

Objective Levodopa in presence of decarboxylase inhibitors is following two-compartment kinetics and its effect is typically modelled using sigmoid Emax models. Pharmacokinetic modelling of the absorption phase of oral distributions is problematic because of irregular gastric emptying. The purpose of this work was to identify and estimate a population pharmacokinetic- pharmacodynamic model for duodenal infusion of levodopa/carbidopa (Duodopa®) that can be used for in numero simulation of treatment strategies. Methods The modelling involved pooling data from two studies and fixing some parameters to values found in literature (Chan et al. J Pharmacokinet Pharmacodyn. 2005 Aug;32(3-4):307-31). The first study involved 12 patients on 3 occasions and is described in Nyholm et al. Clinical Neuropharmacology 2003:26:156-63. The second study, PEDAL, involved 3 patients on 2 occasions. A bolus dose (normal morning dose plus 50%) was given after a washout during night. Plasma samples and motor ratings (clinical assessment of motor function from video recordings on a treatment response scale between -3 and 3, where -3 represents severe parkinsonism and 3 represents severe dyskinesia.) were repeatedly collected until the clinical effect was back at baseline. At this point, the usual infusion rate was started and sampling continued for another two hours. Different structural absorption models and effect models were evaluated using the value of the objective function in the NONMEM package. Population mean parameter values, standard error of estimates (SE) and if possible, interindividual/interoccasion variability (IIV/IOV) were estimated. Results Our results indicate that Duodopa absorption can be modelled with an absorption compartment with an added bioavailability fraction and a lag time. The most successful effect model was of sigmoid Emax type with a steep Hill coefficient and an effect compartment delay. Estimated parameter values are presented in the table. Conclusions The absorption and effect models were reasonably successful in fitting observed data and can be used in simulation experiments.

Predicting entry of Swedish wholesale firms into local markets

Applying microeconomic theory, we develop a forecasting model for firm entry into local markets and test this model using data from the Swedish wholesale industry. The empirical analysis is based on directly estimating the profit function of wholesale firms. As in previous entry studies, profits are assumed to depend on firm- and location-specific factors,and the profit equation is estimated using panel data econometric techniques. Using the residuals from the profit equation estimations, we identify local markets in Sweden where firm profits are abnormally high given the level of all independent variables included in the profit function. From microeconomic theory, we then know that these local markets should have higher net entry than other markets, all else being equal, and we investigate this in a second step,also using a panel data econometric model. The results of estimating the net-entry equation indicate that four of five estimated models have more net entry in high-return municipalities, but the estimated parameter is only statistically significant at conventional levels in one of our estimated models.

Preliminary results concerning the VSS X- chart with unknow in-control parameters

The VSS X- chart is known to perform better than the traditional X- control chart in detecting small to moderate mean shifts in the process. Many researchers have used this chart in order to detect a process mean shift under the assumption of known parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS X- control chart when the process parameters are estimated and we compare them in the case where the process parameters are assumed known. We draw the conclusion that these performances are quite different when the shift and the number of samples used during the phase I are small. ©2010 IEEE.