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[en] MATHEMATICAL MODEL OF AN ELECTROMAGNETIC MULTIVIBRATOR / [es] MODELO MATEMATICO DE UN MULTIVIBRADOR ELECTROMAGNETICOEDGAR BARRIOS URUENA 28 October 2009 (has links)
[en] This work consists in three parts: the first experimental, the second analytical and the third analyticalcomputational. In the first part is found experimentally, the transfer function of an electrodynamic multivibrator, is obtained experimentally by frequencial analysis. The second part presents the development of analytical multivibrator model and from it the analytical transfer function is obtained. Finally, in the third part the author obtains an approximate value of the model’s parameters which are introduced in an optimization’s subroutine to make the parameters. / [es] Este trabajo consta de tres partes: la primera es experimental, la segunda analítica y la tercera es analítica y computacional. En la primera parte es encontrada experimentalmente la función de transferencia de un multivibrador electrodinámico, la cual es obtenida experimentalmente por análisis frecuencial. La segunda parte presenta el desarrollo analítico del modelo del multivibrador y a partir de este modelo es obtenida la función de transferencia analítica. Finalmente, en la tercera parte el autor obtiene un valor aproximado de los parámetros del modelo, los cuales son introducidos en la rutina de optimización que busca mejorar estos parámetros.

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Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients.January 1985 (has links)
by Waiyin Poon. / Bibliography: leaves 6264 / Thesis (M.Ph.)Chinese University of Hong Kong, 1985

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On Measuring agreement for categorical data.January 2002 (has links)
Tang PikHa. / Thesis (M.Phil.)Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 5154). / Abstracts in English and Chinese. / Chapter 1  Introduction  p.1 / Chapter 1.1  Agreement analysis  p.2 / Chapter 1.2  Outline of the thesis  p.6 / Chapter 2  Review  p.8 / Chapter 2.1  Chancecorrected measures  p.8 / Chapter 2.2  Statistical Modelling Approach  p.15 / Chapter 3  Modelbased kappa  p.17 / Chapter 3.1  An agreement model with kappa as parameter  p.17 / Chapter 3.2  Parameter Estimation  p.21 / Chapter 3.3  Asymptotic variancecovariance matrix  p.25 / Chapter 3.3.1  Fisher Information  p.25 / Chapter 3.3.2  Computational detail  p.27 / Chapter 3.4  Illustrative Example  p.30 / Chapter 4  Simulation Study  p.33 / Chapter 4.1  Design  p.33 / Chapter 4.2  Results  p.37 / Chapter 4.3  Discussion  p.40 / Chapter 5  Conclusion  p.42 / Tables  p.44 / Figures  p.49 / Bibliography  p.51

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Maximum likelihood estimation of parameters with constraints in normaland multinomial distributionsXue, Huitian., 薛惠天. January 2012 (has links)
Motivated by problems in medicine, biology, engineering and economics, con
strained parameter problems arise in a wide variety of applications. Among them
the application to the doseresponse of a certain drug in development has attracted
much interest. To investigate such a relationship, we often need to conduct a dose
response experiment with multiple groups associated with multiple dose levels of
the drug. The doseresponse relationship can be modeled by a shaperestricted
normal regression. We develop an iterative twostep ascent algorithm to estimate
normal means and variances subject to simultaneous constraints. Each iteration
consists of two parts: an expectation{maximization (EM) algorithm that is utilized
in Step 1 to compute the maximum likelihood estimates (MLEs) of the restricted
means when variances are given, and a newly developed restricted De Pierro algorithm that is used in Step 2 to find the MLEs of the restricted variances when
means are given. These constraints include the simple order, tree order, umbrella
order, and so on. A bootstrap approach is provided to calculate standard errors of
the restricted MLEs. Applications to the analysis of two real datasets on radioimmunological assay of cortisol and bioassay of peptides are presented to illustrate
the proposed methods.
Liu (2000) discussed the maximum likelihood estimation and Bayesian estimation in a multinomial model with simplex constraints by formulating this
constrained parameter problem into an unconstrained parameter problem in the
framework of missing data. To utilize the EM and data augmentation (DA) algorithms, he introduced latent variables {Zil;Yil} (to be defined later). However,
the proposed DA algorithm in his paper did not provide the necessary individual
conditional distributions of Yil given (the observed data and) the updated parameter estimates. Indeed, the EM algorithm developed in his paper is based on the
assumption that{ Yil} are fixed given values. Fortunately, the EM algorithm is
invariant under any choice of the value of Yil, so the final result is always correct.
We have derived the aforesaid conditional distributions and hence provide a valid
DA algorithm. A real data set is used for illustration. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

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A comparison of informative and discriminative estimation of parameters for classifier training /Goodman, Graham Laurence Unknown Date (has links)
Thesis (PhD)University of South Australia, 2000

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Semiparametric methods in generalized linear models for estimating population size and fatality rateLiu, Danping. January 2005 (has links)
Thesis (M. Phil.)University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

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A Methodology to Estimate Time Varying User Responses to Travel Time and Travel Time Reliability in a Road Pricing EnvironmentAlvarez, Patricio A 29 March 2012 (has links)
Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day.
In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding timevarying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The timevarying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limitedmemory discrete Kalman filter (ALMF).
The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the realtime information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions.
It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of timevarying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a welldefined normal distribution.

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Traitbased Approaches In Aquatic EcologyWerba, Jo January 2020 (has links)
Ecologists try to understand how changing habitats alter the populations of organisms living within them, and how, in turn, these changing populations alter the environment. By linking individual or cellular (physiological) processes to system level responses, mechanistic models can help describe the feedback loops between organisms and the environment. Aquatic systems have long used mechanistic models, but increasing model complexity over the last 50 years has led to difficulty in parameterization. In fact, it is often unclear how researchers are choosing parameters at all, even though small changes in parameters can change qualitative predictions. I explore the challenges in parameter estimation present in even an ideal situation. Specifically, I conduct individual experiments for all of the needed parameters to describe a simple labbased, aquatic system; estimate those parameters using the results from these experiments supplemented with literature data; and run a large experiment designed to test how well the labestimated parameters predict actual zooplankton populations and nutrient changes over time. I document best practices for finding and reporting parameter choices and show whole ecosystem level consequences of a variety of decisions. To get the best predictions I find that a mix of parameter estimation methods are necessary. Traitbased approaches are another method to understand speciesenvironment interactions. Traitbased methods aggregate species into functional traits, perhaps making qualitative predictions easier. Theory suggests that more functionally diverse systems will be more resilient. I test this prediction in a simple aquatic system but am unable to find consistent support for this hypothesis, and instead finding that results are highly dependent on what measures of ecosystem recovery are used. Overall, more speciesspecific information is critical to building better models for both mechanistic and traitbased approaches. I expand speciesspecific data by providing new information, and collating information from literature on a small, tropical Cladocera. / Thesis / Doctor of Philosophy (PhD) / Predicting what will happen to a habitat after a disturbance is critical for conservation and management. Species specific information is useful for building a mechanistic understanding of ecology. Predictions that include underlying processes (mechanisms) may be more robust to a changing environment than predictions based on correlations. Eutrophication, the addition of excess nutrients, is a common problem in freshwater habitats. Being able to predict the effects of nutrient addition is critical for ensuring the health of freshwater ecosystems. By using speciesspecific life history and morphological information and a simple lab system, I test different methods of predicting and understanding the consequences of eutrophication. I find that the ramifications of eutrophication are not easily predicted by species' categorizations or with a more detailed mechanistic model.

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ESTIMATION OF RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE PARAMETERS: SMALL SAMPLE PROPERTIES OF ESTIMATORS.BORGSTROM, MARK CRAIG. January 1987 (has links)
When studying detection systems, parameters associated with the Receiver Operating Characteristic (ROC) curve are often estimated to assess system performance. In some applied settings it is often not possible to test the detection system with large numbers of stimuli. The resulting small sample statistics many have undesirable properties. The characteristics of these small sample ROC estimators were examined in a Monte Carlo simulation. Three popular ROC parameters were chosen for study. One of the parameters was a single parameter index of system performance, Area under the ROC curve. The other parameters, ROC intercept and slope, were considered as a pair. ROC intercept and slope were varied along with sample size and points on the certainty rating scale to form a four way factorial design. Several types of estimators were examined. For the parameter, Area under the curve, Maximum Likelihood (ML), three types of Least Squares (LS), and Distribution Free (DF) estimators were considered. Except for the DF estimator, the same estimators were considered for the parameters, intercept and slope. These estimators were compared with respect to three characteristics: bias, efficiency, and consistency. For Area under the curve, the ML estimator was the least biased. The DF estimator was the most efficient, and all the estimators except the DF estimator appeared to be consistent. For intercept and slope the LS estimator that minimized vertical error of the points from the ROC curve (line) was the least biased for both estimators. This LS estimator was also the most efficient. This estimator along with the ML estimator also appeared to be the most consistent. The other two estimators had no significant trend toward consistency. These results along with other findings, illustrate that different estimators may be "best" for different sample sizes and for different parameters. Therefore, researchers should carefully consider the characteristics of ROC estimators before using them as indices of system performance.

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Model identification and parameter estimation of stochastic linear models.Vazirinejad, Shamsedin. January 1990 (has links)
It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errorsinvariables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errorsinvariables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however, such information is neither available nor obtainable. Although under such circumstances one can not obtain a unique vector of parameters, the space, Ω, of the feasible solutions can be computed. Additionally, assumption of existence of a single linear relationship may be presumptuous as well. A multiequation model similar to the simultaneousequations models of econometrics may be more appropriate. The goals of this dissertation are the following: (1) To present analytical techniques or algorithms to reduce the solution space, Ω, when any type of prior information, exact or relative, is available; (2) The data covariance matrix, Σ, can be examined to determine whether or not Ω is bounded. If Ω is not bounded a multiequation model is more appropriate. The methodology for identifying the subsets of variables within which linear relations can feasibly exist is presented; (3) Ridge regression technique is commonly employed in order to reduce the ills caused by collinearity. This is achieved by perturbing the diagonal elements of Σ. In certain situations, applying ridge regression causes some of the coefficients to change signs. An analytical technique is presented to measure the amount of perturbation required to render such variables ineffective. This information can assist the analyst in variable selection as well as deciding on the appropriate model; (4) For the situations when Ω is bounded, a new weighted regression technique based on the computed upper bounds on the noise variances is presented. This technique will result in identification of a unique estimate of the model parameters.

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