Parameter estimation pages

Selected entries from Methods in Enzymology [vol, page(s)] Computer programs, 240, 312 infrared S-H stretch bands for hemoglobin A, 232, 159-160 determination of enzyme kinetic parameter, 240, 314-319 kinetics program, in finite element analysis of hemoglobin-CO reaction, 232, 523-524, 538-558 nonlinear least-squares method, 240, 3-5, 10 to oxygen equilibrium curve, 232, 559, 563 parameter estimation with Jacobians, 240, 187-191. 

Selected entries from Methods in Enzymology [vol, page(s)] Generation, 240, 122-123 confidence limits, 240, 129-130 discrete variance profile, 240, 124-126, 128-129, 131-133, 146, 149 error response, 240, 125-126, 149-150 Monte Carlo validation, 240, 139, 141, 146, 148-149 parameter estimation, 240, 126-129 radioimmunoassay, 240, 122-123, 125-127, 131-139 standard errors of mean, 240, 135 unknown sample evaluation, 240, 130-131 zero concentration response, 240, 138, 150. 

Pages, A., H. Pingaud, M. Meyer, and X. Joulia (1994), A strategy for simultaneous data reconciliation and parameter estimation on process flowsheets. Computers Chem. Engng. 18, SuppL, S 223 - S 227... 

The objective is to demonstrate the power of modern simulation packages in the estimation of model parameters. Here the parameters are estimated using SIMUSOLV running on VAX systems and on a PC using the ESL simulation package, the main features of which can be found on the last page of this book. 

It must be emphasized that the availability of the SMO and 2D autocovariance function methods as two independent statistical procedures to estimate the same parameter, in, the number of proteins, is a helpful tool to verify the reliability of the results obtained. In the case of the 2D PAGE map of colorectal adenocarcinoma cell line (DL-1) an excellent agreement was found between the values obtained from the SMO method—m = 101 10 and m = 105 10—and the 2D autocovariance function procedure—m = 104 10 (Pietrogrande et al., 2006a). 

Selected entries from Methods in Enzymology [vol, page(s)] Aspartate transcarbamylase [assembly effects, 259, 624-625 buffer sensitivity, 259, 625 ligation effects, 259, 625 mutation effects, 259, 626] baseline estimation [effect on parameters, 240, 542-543, 548-549 importance of, 240, 540 polynomial interpolation, 240, 540-541,549, 567 proportional method for, 240, 541-542, 547-548, 567] baseline subtraction and partial molar heat capacity, 259, 151 changes in solvent accessible surface areas, 240, 519-520, 528 characterization of membrane phase transition, 250,... 

Selected entries from Methods in Enzymology [vol, page(s)] Association constant determination, 259, 444-445 buoyant mass determination, 259, 432-433, 438, 441, 443, 444 cell handling, 259, 436-437 centerpiece selection, 259, 433-434, 436 centrifuge operation, 259, 437-438 concentration distribution, 259, 431 equilibration time, estimation, 259, 438-439 molecular weight calculation, 259, 431-432, 444 nonlinear least-squares analysis of primary data, 259, 449-451 oligomerization state of proteins [determination, 259, 439-441, 443 heterogeneous association, 259, 447-448 reversibility of association, 259, 445-447] optical systems, 259, 434-435 protein denaturants, 259, 439-440 retroviral protease, analysis, 241, 123-124 sample preparation, 259, 435-436 second virial coefficient [determination, 259, 443, 448-449 nonideality contribution, 259, 448-449] sensitivity, 259, 427 stoichiometry of reaction, determination, 259, 444-445 terms and symbols, 259, 429-431 thermodynamic parameter determination, 259, 427, 443-444, 449-451. 

Huber, 1964] Huber, P. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, pages 73-101. 

Miller (2002, pages 148-150) recommended fivefold to tenfold validation, so that effectively 80% to 90% of the data should be in the training set. Another recommendation is that n /4 of the data should make up a training set (randomly selected) and the rest predicted as test hold-out data see Shao (1993) for details. However, it is easy to show that use of the 3/4 rule does not perform well in settings such as drug discovery where prediction accuracy, rather than selection of the true model, is the objective. We are sometimes better off with a model that is not the true model but a simpler model for which we can make good estimates of the parameters (leading to more accurate predicted values). 

The variance-covariance matrix of the estimated parameters will be as shown on the next page. 

EM Jordaan and GF Smits. Estimation of the regularization parameter for support vector regression. In Proc. World Conf. Computational Intelligence, pages 2785-2791, Honolulu, Hawaii, 2002. 

AND/CAS] Anderson, G. M., Castet, S., Schott, J., Mesmer, R. E., The density model for estimation of thermodynamic parameters of reactions at high temperatures and pressures, Geochim. Cosmochim. Acta, 55, (1991), 1769-1779. Cited on page 41. 

This very extensive (99 pages) chapter (no. 2 in Volume II) contains a general discussion of the effects of temperature and pressure on activity coefficients for both binary and mixed electrolyte solutions. Properties of interest are the partial molar volume, expansibility, compressibility, heat capacity, and enthalpy. There is also an excellent discussion of methods of estimating partial molar properties in mixed electrolyte solutions. There are 226 references to the literature. Tables of data are presented for Debye-HUckel limiting law slopes for the afJ parent molar volume, enthalpy, heat capacity, expansibility, and compressibility as a function of temperature parameters for the partial molar volumes of 30 aqueous electrolyes at 25 °C parameters for the partial molar expansibility of ten electrolytes at 25 C parameters for the partial molar compressibilities of 33 electrolytes at 25 °C values of the activity coefficients of aqueous NaCl solutions at 25 C as a function of pressure (up to 1000 bars) parameters for the partial molar enthalpies of 59 electrolytes at 25 C parameters for the partial molar heat capacities of 140 electrolytes at 25 °C and tables giving compositions and the partial molar properties of average seawater. 

An excellent source of adjustable parameters for the Antoine Equation is the reference Yaws C. L., and Yang Fl.-C., "To Estimate Vapor Pressure Easily. Antoine Coefficients Relate Vapor Pressure to Temperature for Almost 700 Major Organic Compounds", Hydrocarbon Processing, Vol. 68, No. 10, 1989, Pages 65 to 68. 

The next step is to go to the Properties list on the left-hand side of the Data Browser window and select Input under Estimation. Clicking the Setup page tab in this window, the default selection is Estimate all missing parameters as shown in Figure 3.65. We go with this default selection and have Aspen estimate all missing parameters. The next step is to enter the molecular strucmre of EMC and also to enter as much physical property information about this new component as is known. 

Lee.C., Gauvain,J. (1993 April). Speaka- Adaptation Base on MAP Estimation of Hidden Markov Model Parameter. Pages 558-561 of Proceeding of IEEE Intmiational Conference on Acoustics Speech and Signal Processing. 

The pump simulation is done by opening a new Hysys case and selecting the components methane, ethane, COj, and Nj. PRSV is selected as the property estimation method. The basis of calculation is assumed as 100 kg/h of natural gas stream. Select a compressor from the object palette and specify the stream conditions and keep the Hysys default compressor efficiency and then determine the outlet temperature and compressor duty. Neglect the heat loss or gain from the environment. Enter the Pressure for the outlet stream which is 10 bars. Click on the Design tab, and then click on Parameters and see the Hysys default adiabatic efficiency and calculated Polytropic efficiency. In the Adiabatic Efficiency box on the parameter page, enter 10 (Figure 2.73). Click on the Worksheet tab to view the results. 

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