Parameter estimation efficiency

Numerical methods used to fit experimental data should, ideally, give parameter estimates that are unbiased with reliable estimates of precision. Therefore, determining the reliability of parameter estimates from simulated PPK studies is an absolute necessity since it may affect study outcome. Not only should bias and precision associated with parameter estimation be determined but also the confidence with which these parameters are estimated should be examined. Confidence interval estimates are a function of bias, standard error of parameter estimates, and the distribution of parameter estimates. Use of an informative design can have a significant impact on increasing precision. Paying attention to these measures of parameter estimation efficiency is critical to a simulation study outcome (6, 7). 

STUDY EXECUTION AND IMPACT ON PARAMETER ESTIMATION EFFICIENCY... 

Serth, R.W, B. Srikanth, and S.J. Maronga, Gross Error Detection and Stage Efficiency Estimation in a Separation Process, AlChE Journal, 39(10), 1993, 1726-1731. (Physical model development, parameter estimation)... 

Three parameters thus need to be estimated, namely the scalar factor a, the compression factor c, and the shift d. Parameter b was dropped for two reasons (1) the effect of this exponent is to be explored, so it must remain fixed during a parameter-fitting calculation, and (2) the parameter estimation decreases in efficiency for every additional parameter. Therefore the model takes on the form... 

The above implicit formulation of maximum likelihood estimation is valid only under the assumption that the residuals are normally distributed and the model is adequate. From our own experience we have found that implicit estimation provides the easiest and computationally the most efficient solution to many parameter estimation problems. 

Given the fact that in parameter estimation we normally have a relatively smooth LS objective function, we do not need to be exceptionally concerned about local optima (although this may not be the case for ill-conditioned estimation problems). This is particularly true if we have a good idea of the range where the parameter values should be. As a result, it may be more efficient to consider using a value for NR which is a function of the number of unknown parameters. For example, we may consider... 

If we have very little information about the parameters, direct search methods, like the LJ optimization technique presented in Chapter 5, present an excellent way to generate very good initial estimates for the Gauss-Newton method. Actually, for algebraic equation models, direct search methods can be used to determine the optimum parameter estimates quite efficiently. However, if estimates of the uncertainty in the parameters are required, use of the Gauss-Newton method is strongly recommended, even if it is only for a couple of iterations. 

Given an EoS, the objective of the parameter estimation problem is to compute optimal values for the interaction parameter vector, k, in a statistically correct and computationally efficient manner. Those values are expected to enhance the correlational ability of the EoS without compromising its ability to predict the correct phase behavior. 

The implicit LS, ML and Constrained LS (CLS) estimation methods are now used to synthesize a systematic approach for the parameter estimation problem when no prior knowledge regarding the adequacy of the thermodynamic model is available. Given the availability of methods to estimate the interaction parameters in equations of state there is a need to follow a systematic and computationally efficient approach to deal with all possible cases that could be encountered during the regression of binary VLE data. The following step by step systematic approach is proposed (Englezos et al. 1993)... 

The PULSAR units are high efficiency static aerators that have been developed for municipal wastewater treatment plants and have successfully been used over extended periods of time without any operational problems such as unstable operation or plugging up during intermittent operation of the air pumps (Chourda-kis, 1999). Data have been collected from a pilot plant unit at the Wastewater Treatment plant of the Industrial Park (Herakleion, Crete). A series of experiments were conducted for the determination of the mass transfer coefficient (kLa) and are shown in Figure 17.4. The data are also available in tabular form as part of the parameter estimation input files provided with the enclosed CD. 

The solution developed is able to solve the scheduling problem very efficiently, resulting in good and realistic schedules. Of course, the solution quality depends to a great deal on how well the parameter estimation matches with the production process. More illustrations on the solution can be found in [5]. 

The relative efficiencies have also been obtained by comparing these relative errors to the smallest value of the other estimates for each case studied. The smaller the relative error, the better the model parameter estimation the larger the relative efficiency, the better the estimator. Results are listed in Table 1 for the uniform and t2 distributions. 

Many of the models encountered in reaction modeling are not linear in the parameters, as was assumed previously through Eq. (20). Although the principles involved are very similar to those of the previous subsections, the parameter-estimation procedure must now be iteratively applied to a nonlinear surface. This brings up numerous complications, such as initial estimates of parameters, efficiency and effectiveness of convergence algorithms, multiple minima in the least-squares surface, and poor surface conditioning. 

Central composite designs are relatively efficient for small numbers of factors. Efficiency in this case means obtaining the required parameter estimates with little wasted effort. One measure of efficiency is the efficiency value, E... 

The first two sections of Chapter 5 give a practical introduction to dynamic models and their numerical solution. In addition to some classical methods, an efficient procedure is presented for solving systems of stiff differential equations frequently encountered in chemistry and biology. Sensitivity analysis of dynamic models and their reduction based on quasy-steady-state approximation are discussed. The second central problem of this chapter is estimating parameters in ordinary differential equations. An efficient short-cut method designed specifically for PC s is presented and applied to parameter estimation, numerical deconvolution and input determination. Application examples concern enzyme kinetics and pharmacokinetic compartmental modelling. 

In a strict sense parameter estimation is the procedure of computing the estimates by localizing the extremum point of an objective function. A further advantage of the least squares method is that this step is well supported by efficient numerical techniques. Its use is particularly simple if the response function (3.1) is linear in the parameters, since then the estimates are found by linear regression without the inherent iteration in nonlinear optimization problems. 

These partial derivatives provide a lot of information (ref. 10). They show how parameter perturbations (e.g., uncertainties in parameter values) affect the solution. Identifying the unimportant parameters the analysis may help to simplify the model. Sensitivities are also needed by efficient parameter estimation procedures of the Gauss - Newton type. Since the solution y(t,p) is rarely available in analytic form, calculation of the coefficients Sj(t,p) is not easy. The simplest method is to perturb the parameter pj, solve the differential equation with the modified parameter set and estimate the partial derivatives by divided differences. This "brute force" approach is not only time consuming (i.e., one has to solve np+1 sets of ny differential equations), but may be rather unreliable due to the roundoff errors. A much better approach is solving the sensitivity equations... 

In summary while conceptually appealing, the application of complex multi-solute models for Sr sorption to zeolite is in the early stages of development. While preliminary results are encouraging, additional work is required to develop more efficient computational methods and develop an improved database for parameter estimation. The remainder of this section focuses on the simpler retardation factor approach. 

The random variable values 0 are more centered around the population parameter than the 02 ones (i.e. estimations). This means that the average error made in multiple population parameter estimation by means of 0 will be smaller than when we do the same for 02. The 0 estimation can be said to be more efficient. 

Consider all alternatives for estimation in terms of reliability, accuracy, time required, and cost efficiency. Develop predictive models that allow for in silico screening, rather than necessitating prior synthesis of compound. Analyze literature for both pharmacokinetic and toxicokinetic parameter estimation, to identify models that already exist or ones that could be suitably modified for the parameter of interest... 

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