Relevant bibliographies by topics / Parameter estimates

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Parameter estimates'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Parameter estimates"

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Mead, J. L. "Discontinuous parameter estimates with least squares estimators." Applied Mathematics and Computation 219, no. 10 (January 2013): 5210–23. http://dx.doi.org/10.1016/j.amc.2012.11.067.

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Pilling, Graham M., Geoffrey P. Kirkwood, and Stephen G. Walker. "An improved method for estimating individual growth variability in fish, and the correlation between von Bertalanffy growth parameters." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 3 (March 1, 2002): 424–32. http://dx.doi.org/10.1139/f02-022.

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A new method for estimating individual variability in the von Bertalanffy growth parameters of fish species is presented. The method uses a nonlinear random effects model, which explicitly assumes that an individual's growth parameters represent samples from a multivariate population of growth parameters characteristic of a species or population. The method was applied to backcalculated length-at-age data from the tropical emperor, Lethrinus mahsena. Individual growth parameter variability estimates were compared with those derived using the current "standard" method, which characterizes the joint distribution of growth parameter estimates obtained by independently fitting a growth curve to each individual data set. Estimates of mean von Bertalanffy growth parameters from the two methods were similar. However, estimated growth parameter variances were much higher using the standard method. Using the random effects model, the estimated correlation between population mean values of L[Formula: see text] and K was –0.52 or –0.42, depending on the marginal distribution assumed for K. The latter estimate had a 95% posterior credibility interval of –0.62 to –0.17. These represent the first reliable estimate of this correlation and confirm the view that these parameters are negatively correlated in fish populations; however, the absolute correlation value is somewhat lower than has been assumed.
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Coventry, William L., and Matthew C. Keller. "Estimating the Extent of Parameter Bias in the Classical Twin Design: A Comparison of Parameter Estimates From Extended Twin-Family and Classical Twin Designs." Twin Research and Human Genetics 8, no. 3 (June 1, 2005): 214–23. http://dx.doi.org/10.1375/twin.8.3.214.

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AbstractThe classical twin design (CTD) circumvents parameter indeterminacy by assuming (1) negligible higher-order epistasis; and (2) either nonadditive genetic or common environmental effects are nonexistent, creating two potential sources of bias (Eaves et al., 1978; Grayson, 1989). Because the extended twin-family design (ETFD) uses many more unique covariance observations to estimate parameters, common environmental and nonadditive genetic parameters can be simultaneously estimated. The ETFD thereby corrects for what is likely to be the largest of the two sources of bias in CTD parameter estimates (Keller & Coventry, 2005). In the current paper, we assess the extent of this and other potential sources of bias in the CTD by comparing all published ETFD parameter estimates to CTD parameter estimates derived from the same data. CTD estimates of the common environment were lower than ETFD estimates of the common environment for some phenotypes, but for other phenotypes (e.g., stature in females and certain social attitudes), what appeared as the common environment was resolved to be assortative mating in the ETFD. On average, CTD estimates of nonadditive genetic factors were 43% lower, and additive genetic factors 63% higher, than ETFD estimates. However, broad-sense heritability estimates from the CTD were only 18% higher than ETFD estimates, highlighting that the CTD is useful for estimating broad-sense but not narrow-sense heritability. These results suggest that CTD estimates can be misleading when interpreted literally, but useful, albeit coarse, when interpreted properly.
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Rajanayaka, Channa, and Don Kulasiri. "Investigation of a parameter estimation method for contaminant transport in aquifers." Journal of Hydroinformatics 3, no. 4 (October 1, 2001): 203–13. http://dx.doi.org/10.2166/hydro.2001.0019.

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Real world groundwater aquifers are heterogeneous and system variables are not uniformly distributed across the aquifer. Therefore, in the modelling of the contaminant transport, we need to consider the uncertainty associated with the system. Unny presented a method to describe the system by stochastic differential equations and then to estimate the parameters by using the maximum likelihood approach. In this paper, this method was explored by using artificial and experimental data. First a set of data was used to explore the effect of system noise on estimated parameters. The experimental data was used to compare the estimated parameters with the calibrated results. Estimates obtained from artificial data show reasonable accuracy when the system noise is present. The accuracy of the estimates has an inverse relationship to the noise. Hydraulic conductivity estimates in a one-parameter situation give more accurate results than in a two-parameter situation. The effect of the noise on estimates of the longitudinal dispersion coefficient is less compared to the effect on hydraulic conductivity estimates. Comparison of the results of the experimental dataset shows that estimates of the longitudinal dispersion coefficient are similar to the aquifer calibrated results. However, hydraulic conductivity does not provide a similar level of accuracy. The main advantage of the estimation method presented here is its direct dependence on field observations in the presence of reasonably large noise levels.
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Yi, Kyongsu, and Karl Hedrick. "Observer-Based Identification of Nonlinear System Parameters." Journal of Dynamic Systems, Measurement, and Control 117, no. 2 (June 1, 1995): 175–82. http://dx.doi.org/10.1115/1.2835177.

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This paper deals with an observer-based nonlinear system parameter identification method utilizing repetitive excitation. Although methods for physical parameter identification of both linear and nonlinear systems are already available, they are not attractive from a practical point of view since the methods assume that all the system, x, and the system input are available. The proposed method is based on a “sliding observer” and a least-square method. A sufficient condition for the convergence of the parameter estimates is provided in the case of “Lipschitz” nonlinear second-order systems. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system with repetitive excitation, the parameter estimates are obtained. The observer based identification method has been tested on a half car simulation and used to identify the parameters of a half car suspension test rig. The estimates of nonlinear damping coefficients of a vehicle suspension, suspension stiffness, pitch moment inertia, equivalent sprung mass, and unsprung mass are obtained by the proposed method. Simulation and experimental results show that the identifier estimates the vehicle parameters accurately. The proposed identifier will be useful for parameter identification of actual vehicles since vehicle parameters can be identified only using vehicle excitation tests rather than component testing.
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Luo, Yong, and Dimiter M. Dimitrov. "A Short Note on Obtaining Point Estimates of the IRT Ability Parameter With MCMC Estimation in Mplus: How Many Plausible Values Are Needed?" Educational and Psychological Measurement 79, no. 2 (May 29, 2018): 272–87. http://dx.doi.org/10.1177/0013164418777569.

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Plausible values can be used to either estimate population-level statistics or compute point estimates of latent variables. While it is well known that five plausible values are usually sufficient for accurate estimation of population-level statistics in large-scale surveys, the minimum number of plausible values needed to obtain accurate latent variable point estimates is unclear. This is especially relevant when an item response theory (IRT) model is estimated with MCMC (Markov chain Monte Carlo) methods in Mplus and point estimates of the IRT ability parameter are of interest, as Mplus only estimates the posterior distribution of each ability parameter. In order to obtain point estimates of the ability parameter, a number of plausible values can be drawn from the posterior distribution of each individual ability parameter and their mean (the posterior mean ability estimate) can be used as an individual ability point estimate. In this note, we conducted a simulation study to investigate how many plausible values were needed to obtain accurate posterior mean ability estimates. The results indicate that 20 is the minimum number of plausible values required to obtain point estimates of the IRT ability parameter that are comparable to marginal maximum likelihood estimation(MMLE)/expected a posteriori (EAP) estimates. A real dataset was used to demonstrate the comparison between MMLE/EAP point estimates and posterior mean ability estimates based on different number of plausible values.
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Fieberg, J., and D. F. Staples. "The role of variability and uncertainty in testing hypotheses involving parameters in stochastic demographic models." Canadian Journal of Zoology 84, no. 11 (November 2006): 1698–701. http://dx.doi.org/10.1139/z06-153.

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Hierarchical / random effect models provide a statistical framework for estimating variance parameters that describe temporal and spatial variability of vital rates in population dynamic models. In practice, estimates of variance parameters (e.g., process error) from these models are often confused with estimates of uncertainty about model parameter estimates (e.g., standard errors). These two sources of “error” have different implications for predictions from stochastic models. Estimates of process error (or variability) are useful for describing the magnitude of variation in vital rates over time and are a feature of the modeled process itself, whereas estimates of parameter standard errors (or uncertainty) are necessary for interpreting how well we are able to estimate model parameters and whether they differ among groups. The goal of this comment is to illustrate these concepts in the context of a recent paper by A.W. Reed and N.A. Slade (Can. J. Zool. 84: 635–642 (2006)) . In particular, we will show that their “hypothesis tests” involving mean parameters are actually comparisons of the estimated distributions of vital rates among groups of individuals.
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Koots, Kenneth R., and John P. Gibson. "Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations." Genetics 143, no. 3 (July 1, 1996): 1409–16. http://dx.doi.org/10.1093/genetics/143.3.1409.

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Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.
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Riswan, Riswan. "Sample Size and Test Length for Item Parameter Estimate and Exam Parameter Estimate." Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam 9, no. 1 (December 26, 2021): 69–78. http://dx.doi.org/10.24256/jpmipa.v9i1.2384.

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The Item Response Theory (IRT) model contains one or more parameters in the model. These parameters are unknown, so it is necessary to predict them. This paper aims (1) to determine the sample size (N) on the stability of the item parameter (2) to determine the length (n) test on the stability of the estimate parameter examinee (3) to determine the effect of the model on the stability of the item and the parameter to examine (4) to find out Effect of sample size and test length on item stability and examinee parameter estimates (5) Effect of sample size, test length, and model on item stability and examinee parameter estimates. This paper is a simulation study in which the latent trait (q) sample simulation is derived from a standard normal population of ~ N (0.1), with a specific Sample Size (N) and test length (n) with the 1PL, 2PL and 3PL models using Wingen. Item analysis was carried out using the classical theory test approach and modern test theory. Item Response Theory and data were analyzed through software R with the ltm package. The results showed that the larger the sample size (N), the more stable the estimated parameter. For the length test, which is the greater the test length (n), the more stable the estimated parameter (q).
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Jiang, Renyan. "A Quasi-Normal Distribution and its Application in Parameter Estimation on Heavily Censored Data." International Journal of Reliability, Quality and Safety Engineering 28, no. 05 (May 5, 2021): 2150027. http://dx.doi.org/10.1142/s0218539321500273.

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Parameter estimation on heavily censored data is a challenging issue. This paper addresses this issue using a two-step single-parameter maximum likelihood method to estimate mean time to failure (MTTF) and Weibull shape parameter (WSP). The first step fits the data to three one-parameter auxiliary models, which are special cases of a two-parameter quasi-normal distribution with nonnegative support, to obtain three estimates of the MTTF. The second step estimates the WSP through fixing each of the MTTF estimates. The best estimates are selected from three pairs of estimates based on appropriate rules. A simple numerical method is also proposed to construct the confidence intervals of the estimated MTTF and WSP. The auxiliary models are derived, the model selection rules are specified, and a numerical experiment is carried out to illustrate the accuracy of the proposed method.
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Dissertations / Theses on the topic "Parameter estimates"

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Munster, Drayton William. "Robust Parameter Inversion Using Stochastic Estimates." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96399.

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For parameter inversion problems governed by systems of partial differential equations, such as those arising in Diffuse Optical Tomography (DOT), even the cost of repeated objective function evaluation can be overwhelming. Despite the linear (in the state variable) nature of the DOT problem, the nonlinear parameter inversion process is dominated by the computational burden of solving a large linear system for each source and frequency. To compute the Jacobian for use in Newton-type methods, an adjoint solve is required for each detector and frequency. When a three-dimensional tomography problem may have nearly 1,000 sources and detectors, the computational cost of an optimization routine is a large burden. While techniques from model order reduction can partially alleviate the computational cost, obtaining error bounds in parameter space is typically not feasible. In this work, we examine two different remedies based on stochastic estimates of the objective function. In the first manuscript, we focus on maximizing the efficiency of using stochastic estimates by replacing our objective function with a surrogate objective function computed from a reduced order model (ROM). We use as few as a single sample to detect a misfit between the full-order and surrogate objective functions. Once a sufficiently large difference is detected, it is necessary to update the ROM to reduce the error. We propose a new technique for improving the ROM with very few large linear solutions. Using this techniques, we observe a reduction of up to 98% in the number of large linear solutions for a three-dimensional tomography problem. In the second manuscript, we focus on establishing a robust algorithm. We propose a new trust region framework that replaces the objective function evaluations with stochastic estimates of the improvement factor and the misfit between the model and objective function gradients. If these estimates satisfy a fixed multiplicative error bound with a high, but fixed, probability, we show that this framework converges almost surely to a stationary point of the objective function. We derive suitable bounds for the DOT problem and present results illustrating the robust nature of these estimates with only 10 samples per iteration.
Doctor of Philosophy
For problems such as medical imaging, the process of reconstructing the state of a system from measurement data can be very expensive to compute. The ever increasing need for high accuracy requires very large models to be used. Reducing the computational burden by replacing the model with a specially constructed smaller model is an established and effective technique. However, it can be difficult to determine how well the smaller model matches the original model. In this thesis, we examine two techniques for estimating the quality of a smaller model based on randomized combinations of sources and detectors. The first technique focuses on reducing the computational cost as much as possible. With the equivalent of a single randomized source, we show that this estimate is an effective measure of the model quality. Coupled with a new technique for improving the smaller model, we demonstrate a highly efficient and robust method. The second technique prioritizes robustness in its algorithm. The algorithm uses these randomized combinations to estimate how the observations change for different system states. If these estimates are accurate with a high probability, we show that this leads to a method that always finds a minimum misfit between predicted values and the observed data.
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Tao, Zuoyu. "Improved uncertainty estimates for geophysical parameter retrieval." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61516.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 167-169).
Algorithms for retrieval of geophysical parameters from radiances measured by instruments onboard satellites play a large role in helping scientists monitor the state of the planet. Current retrieval algorithms based on neural networks are superior in accuracy and speed compared to physics-based algorithms like iterated minimum variance (IMV). However, they do not have any form of error estimation, unlike IMV. This thesis examines the suitability of several different approaches to adding in confidence intervals and other methods of error estimation to the retrieval algorithm, as well as alternative machine learning methods that can both retrieve the parameters desired and assign error bars. Test datasets included both current generation operational instruments like AIRS/AMSU, as well as a hypothetical future hyper- spectral microwave sounder. Mixture density networks (MDN) and Sparse Pseudo Input Gaussian processes (SPGP) were found to be the most accurate at variance prediction. Both of these are novel methods in the field of remote sensing. MDNs also had similar training and testing time to neural networks, while SPGPs often took three times as long to train in typical cases. As a baseline, neural networks trained to estimate variance were also tested, but found to be lacking in accuracy and reliability compared to the other methods.
by Zuoyu Tao.
M.Eng.
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Hu, Huilin. "Large sample theory for pseudo-maximum likelihood estimates in semiparametric models /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8936.

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Courdin, Marie Claire. "Laboratory reactor design and the precision of parameter estimates." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7951.

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This study is concerned with investigating the dependence of the precision of estimated kinetic parameters on the type of reactor used for performing the kinetic measurements. Two ideal reactors, the plug-flow reactor (PFR) and the continuous-stirred-tank reactor (CSTR), were simulated using a Monte-Carlo computer simulation. Parameters were estimated using nonlinear multiresponse estimation techniques, and the distributional characteristics of the parameter estimates were calculated. Comparison between the reactors involved the study of overall measures of precision such as the size, shape and orientation of the 95% joint confidence region, and the determinant of the covariance matrix of the parameter estimates. Five variables were identified as having a possible affect on the precision: the nature of the reaction network, the kinetic model, the magnitudes of the rate parameters, the covariance structure of the responses, and the experimental design. The dependence of parameter precision on these variables is presented along with recommendations for determining the reactor type to give the most precise kinetic parameters.
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Wall, Nathan Lane. "Augmented testing and effects on item and proficiency estimates in different calibration designs." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1100.

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Broadening the term augmented testing to include a combination of multiple measures to assess examinee performance on a single construct, the issues of IRT item parameter and proficiency estimates were investigated. The intent of this dissertation is to determine if different IRT calibration designs result in differences to item and proficiency parameter estimates and to understand the nature of those differences. Examinees were sampled from a testing program in which each examinee was administered three mathematics assessments measuring a broad mathematics domain at the high school level. This sample of examinees was used to perform a real data analysis to investigate the item and proficiency estimates. A simulation study was also conducted based upon the real data. The factors investigated for the real data study included three IRT calibration designs and two IRT models. The calibration designs included: separately calibrating each assessment, calibrating all assessments in one joint calibration, and separately calibrating items in three distinct content areas. Joint calibration refers to the use of IRT methodology to calibrate two or more tests, which have been administered to a single group, together so as to place all of the items on a common scale. The two IRT models were the one- and three-parameter logistic model. Also investigated were five proficiency estimators: maximum likelihood estimates, expected a posteriori, maximum a posteriori, summed-score EAP, and test characteristic curve estimates. The simulation study included the same calibration designs and IRT models but the data were simulated with varying levels of correlations among the proficiencies to determine the affect upon the item parameter estimates. The main findings indicate that item parameter and proficiency estimates are affected by the IRT calibration design. The discrimination parameter estimates of the three-parameter model were larger when calibrated under the joint calibration design for one assessment but not for the other two. Noting that equal item discrimination is an assumption of the 1-PL model, this finding raises questions as to the degree of model fit when the 1-PL model is used. Items on a second assessment had lower difficulty parameters in the joint calibration design while the item parameter estimates of the other two assessments were higher. Differences in proficiency estimates between calibration designs were also discovered, which were found to result in examinees being inconsistently classified into performance categories. Differences were observed in regards to the choice of IRT model. Finally, as the level of correlation among proficiencies increased in the simulation data, the differences observed in the item parameter estimates were decreased. Based upon the findings, IRT item parameter estimates resulting from differing calibrations designs should not be used interchangeably. Practitioners who use item pools should base the pool refreshment calibration design upon the one used to originally create the pool. Limitations to this study include the use of a single dataset consisting of high school examinees in only one subject area, thus the degree of generalization regarding research findings to other content areas of grade levels should be made with caution.
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Fernandes, Tamara. "Genetic parameter estimates for ultrasound-measured carcass traits in sheep." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0024/MQ51063.pdf.

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Piwonski, Jaroslaw [Verfasser]. "Parameter estimates for marine ecosystem models in 3-D / Jaroslaw Piwonski." Kiel : Universitätsbibliothek Kiel, 2015. http://d-nb.info/107021874X/34.

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Parekh, Namita. "Validity and efficiency of parameter estimates in frequency matched case-control studies." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq45405.pdf.

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Devitt, Crosby Jordan Blake. "Genetic parameter estimates for finished steer carcass and yearling bull ultrasound measurements." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0026/MQ51058.pdf.

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Man, Peter Lau Weilen. "Statistical methods for computing sensitivities and parameter estimates of population balance models." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608291.

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Books on the topic "Parameter estimates"

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Chu, Chia-Shang J. The moving-estimates test for parameter stability. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1993.

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McAvinchey, Ian D. Income effects on mortality rates: Estimates from a varying parameter model. Aberdeen: University of Aberdeen. Department of Economics, 1987.

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Morelli, Eugene A. Determining the accuracy of maximum likelihood parameter estimates with colored residuals. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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Parekh, Namita. Validity and efficiency of parameter estimates in frequency matched case-control studies. Ottawa: National Library of Canada, 1999.

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Glas, Cees A. W. Alternative approaches to updating item parameter estimates in tests with item cloning. Newtown, PA: Law School Admission Council, 2006.

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Chu, Chia-Shang J. A moving-estimates test for parameter stability and its boundary-crossing probability. Champaign: University of Illinois at Urbana-Champaign, 1992.

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Ackerman, Terry A. The use of unidimensional item parameter estimates of multidimensional items in adaptive testing. Iowa City, Iowa: American College Testing Program, 1988.

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Seber, G. A. F. The estimation of animal abundance and related parameters. 2nd ed. London: Edward Arnold, 1994.

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Hanson, Bradley A. Method of moments estimates for the four-parameter beta compound binomial model and the calculation of classification consistency indexes. Iowa City, Iowa: American College Testing Program, 1991.

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Durham, J. Benson. Jump-diffusion processes and affine term structure models: Additional closed-form approximate solutions, distributional assumptions for jumps, and parameter estimates. Washington, D.C: Federal Reserve Board, 2005.

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Book chapters on the topic "Parameter estimates"

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Akinbogun, Solomon Pelumi, Clinton Aigbavboa, Trynos Gumbo, and Wellington Thwala. "Discussion of Parameter Estimates." In Modelling the Socio-Economic Implications of Sustainability Issues in the Housing Market, 145–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48954-0_8.

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Masters, Timothy. "Resampling for Assessing Parameter Estimates." In Assessing and Improving Prediction and Classification, 101–84. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3336-8_3.

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Kalmijn, Wim. "Happiness Population Distribution Parameter Estimates." In Encyclopedia of Quality of Life and Well-Being Research, 2678–81. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_3657.

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Komornik, Vilmos. "Decay Estimates for the Wave Equation." In Control and Optimal Design of Distributed Parameter Systems, 153–69. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4613-8460-1_7.

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Dorren, H. J. S., and R. K. Snieder. "Stability Estimates for Inverse Problems." In Parameter Identification and Inverse Problems in Hydrology, Geology and Ecology, 213–24. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1704-0_13.

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Speyer, Gavriel, and Michael Werman. "Parameter Estimates for a Pencil of Lines: Bounds and Estimators." In Computer Vision — ECCV 2002, 432–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47969-4_29.

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Becker, Roland, and Boris Vexler. "A Posteriori Error Estimates for Parameter Identification." In Numerical Mathematics and Advanced Applications, 131–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18775-9_10.

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Xiang, Yanyong, Neil R. Thomson, and Jonathan F. Sykes. "L1 and L2 Estimators in Groundwater Problems: Parameter Estimates and Covariances." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 163–73. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1072-3_13.

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Reckase, Mark D. "Transforming Parameter Estimates to a Specified Coordinate System." In Multidimensional Item Response Theory, 233–73. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-89976-3_8.

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Palejev, Dean. "Numerical Parameter Estimates of Beta-Uniform Mixture Models." In Large-Scale Scientific Computing, 472–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97549-4_54.

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Conference papers on the topic "Parameter estimates"

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Spall, J. C. "Seesaw method for combining parameter estimates." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1657540.

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Spall, J. C. "Seesaw method for combining parameter estimates." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591947.

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Leland, R. "Approximate maximum likelihood parameter estimates for stochastic distributed parameter systems." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609518.

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Pike, H. Alan, Larry B. Stotts, Paul Kolodzy, and Malcolm Northcott. "Parameter Estimates For Free Space Optical Communications." In Applications of Lasers for Sensing and Free Space Communications. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/lsc.2011.lwb3.

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Bautista, E., T. S. Strelkoff, A. J. Clemmens, and D. Zerihun. "Surface Volume Estimates for Infiltration Parameter Estimation." In World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)84.

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Kamasak, Mustafa E. "Analysis of kinetic parameter estimates for dynamic PET." In 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011. http://dx.doi.org/10.1109/siu.2011.5929829.

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Emery, A. F., and D. Bardot. "Parameter Estimation for Highly Nonlinear Models With Noisy Data." In ASME 2003 Heat Transfer Summer Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/ht2003-47193.

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Thermal properties are generally determined through solving inverse problems. Because the temperature is a non-linear function of the properties, the solutions are usually effected by linearizing the equations. The statistics of these linearized estimates are based upon the assumptions that the measurement noise has zero mean and is normally distributed, yielding unbiased and normally distributed parameter estimates. In fact, even for this type of noise, nonlinear functions can lead to bias and nonnormal distributions of estimated properties. We examine these effects and show that for typical thermal systems that while the estimates are unbiased and normal, the confidence limits may be inaccurately defined and the residuals of the fits may not be zero mean and uncorrelated. Characterizing the estimated parameters is critical when nonlinear models are to be used for extrapolation.
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Erb, Robert S., and George S. Michaels. "Sensitivity of Biological Models to Errors in Parameter Estimates." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814447300_0006.

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E. Downton, J., D. Gray, and T. Zuk. "Visualizing AVAZ Parameter Estimates with Uncertainty Due to Noise." In 69th EAGE Conference and Exhibition incorporating SPE EUROPEC 2007. European Association of Geoscientists & Engineers, 2007. http://dx.doi.org/10.3997/2214-4609.201401561.

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Gravel, P., J. Verhaeghe, and A. J. Reader. "Impact of fully 4D reconstruction on kinetic parameter estimates." In 2009 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/nssmic.2009.5401812.

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Reports on the topic "Parameter estimates"

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Andrews, Isaiah, Matthew Gentzkow, and Jesse Shapiro. Measuring the Sensitivity of Parameter Estimates to Estimation Moments. Cambridge, MA: National Bureau of Economic Research, November 2014. http://dx.doi.org/10.3386/w20673.

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Powell, Frederic D. Effects of Parameter Uncertainties on Software Development Effort Estimates. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada223304.

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Santini, D., and A. Vyas. Theoretical basis and parameter estimates for the Minority Transportation Expenditure Allocation Model (MITRAM). Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/6052439.

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Schwab, Clint R., and Thomas J. Baas. Genetic Parameter Estimates of Production, Meat Quality, and Sensory Traits in Duroc Swine. Ames (Iowa): Iowa State University, January 2008. http://dx.doi.org/10.31274/ans_air-180814-64.

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Martin, R. D., and Victor J. Yohai. Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada200632.

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Martin, R. D., and Victor J. Yohai. Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada198962.

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Hassen, Abebe T., Doyle E. Wilson, Gene H. Rouse, and Richard G. Tait. Trends in Genetic Parameter Estimates for Ultrasound Back Fat and Rump Fat Thickness Measures in Angus Bulls and Heifers. Ames (Iowa): Iowa State University, January 2004. http://dx.doi.org/10.31274/ans_air-180814-453.

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Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, April 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.

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When fitting observations from a complex survey, the standard regression model assumes that the expected value of the difference between the dependent variable and its model-based prediction is zero, regardless of the values of the explanatory variables. A rarely failing extended regression model assumes only that the model error is uncorrelated with the model’s explanatory variables. When the standard model holds, it is possible to create alternative analysis weights that retain the consistency of the model-parameter estimates while increasing their efficiency by scaling the inverse-probability weights by an appropriately chosen function of the explanatory variables. When a regression model is used to impute for missing item values in a complex survey and when item missingness is a function of the explanatory variables of the regression model and not the item value itself, near unbiasedness of an estimated item mean requires that either the standard regression model for the item in the population holds or the analysis weights incorporate a correctly specified and consistently estimated probability of item response. By estimating the parameters of the probability of item response with a calibration equation, one can sometimes account for item missingness that is (partially) a function of the item value itself.
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Holland, Darren, and Nazmina Mahmoudzadeh. Foodborne Disease Estimates for the United Kingdom in 2018. Food Standards Agency, January 2020. http://dx.doi.org/10.46756/sci.fsa.squ824.

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In February 2020 the FSA published two reports which produced new estimates of foodborne norovirus cases. These were the ‘Norovirus Attribution Study’ (NoVAS study) (O’Brien et al., 2020) and the accompanying internal FSA technical review ‘Technical Report: Review of Quantitative Risk Assessment of foodborne norovirus transmission’ (NoVAS model review), (Food Standards Agency, 2020). The NoVAS study produced a Quantitative Microbiological Risk Assessment model (QMRA) to estimate foodborne norovirus. The NoVAS model review considered the impact of using alternative assumptions and other data sources on these estimates. From these two pieces of work, a revised estimate of foodborne norovirus was produced. The FSA has therefore updated its estimates of annual foodborne disease to include these new results and also to take account of more recent data related to other pathogens. The estimates produced include: •Estimates of GP presentations and hospital admissions for foodbornenorovirus based on the new estimates of cases. The NoVAS study onlyproduced estimates for cases. •Estimates of foodborne cases, GP presentations and hospital admissions for12 other pathogens •Estimates of unattributed cases of foodborne disease •Estimates of total foodborne disease from all pathogens Previous estimates An FSA funded research project ‘The second study of infectious intestinal disease in the community’, published in 2012 and referred to as the IID2 study (Tam et al., 2012), estimated that there were 17 million cases of infectious intestinal disease (IID) in 2009. These include illness caused by all sources, not just food. Of these 17 million cases, around 40% (around 7 million) could be attributed to 13 known pathogens. These pathogens included norovirus. The remaining 60% of cases (equivalent to 10 million cases) were unattributed cases. These are cases where the causal pathogen is unknown. Reasons for this include the causal pathogen was not tested for, the test was not sensitive enough to detect the causal pathogen or the pathogen is unknown to science. A second project ‘Costed extension to the second study of infectious intestinal disease in the community’, published in 2014 and known as IID2 extension (Tam, Larose and O’Brien, 2014), estimated that there were 566,000 cases of foodborne disease per year caused by the same 13 known pathogens. Although a proportion of the unattributed cases would also be due to food, no estimate was provided for this in the IID2 extension. New estimates We estimate that there were 2.4 million cases of foodborne disease in the UK in 2018 (95% credible intervals 1.8 million to 3.1 million), with 222,000 GP presentations (95% Cred. Int. 150,000 to 322,000) and 16,400 hospital admissions (95% Cred. Int. 11,200 to 26,000). Of the estimated 2.4 million cases, 0.9 million (95% Cred. Int. 0.7 million to 1.2 million) were from the 13 known pathogens included in the IID2 extension and 1.4 million1 (95% Cred. Int. 1.0 million to 2.0 million) for unattributed cases. Norovirus was the pathogen with the largest estimate with 383,000 cases a year. However, this estimate is within the 95% credible interval for Campylobacter of 127,000 to 571,000. The pathogen with the next highest number of cases was Clostridium perfringens with 85,000 (95% Cred. Int. 32,000 to 225,000). While the methodology used in the NoVAS study does not lend itself to producing credible intervals for cases of norovirus, this does not mean that there is no uncertainty in these estimates. There were a number of parameters used in the NoVAS study which, while based on the best science currently available, were acknowledged to have uncertain values. Sensitivity analysis undertaken as part of the study showed that changes to the values of these parameters could make big differences to the overall estimates. Campylobacter was estimated to have the most GP presentations with 43,000 (95% Cred. Int. 19,000 to 76,000) followed by norovirus with 17,000 (95% Cred. Int. 11,000 to 26,000) and Clostridium perfringens with 13,000 (95% Cred. Int. 6,000 to 29,000). For hospital admissions Campylobacter was estimated to have 3,500 (95% Cred. Int. 1,400 to 7,600), followed by norovirus 2,200 (95% Cred. Int. 1,500 to 3,100) and Salmonella with 2,100 admissions (95% Cred. Int. 400 to 9,900). As many of these credible intervals overlap, any ranking needs to be undertaken with caution. While the estimates provided in this report are for 2018 the methodology described can be applied to future years.
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Kott, Phillip S. Better Coverage Intervals for Estimators from a Complex Sample Survey. RTI Press, February 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0041.2002.

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Coverage intervals for a parameter estimate computed using complex survey data are often constructed by assuming the parameter estimate has an asymptotically normal distribution and the measure of the estimator’s variance is roughly chi-squared. The size of the sample and the nature of the parameter being estimated render this conventional “Wald” methodology dubious in many applications. I developed a revised method of coverage-interval construction that “speeds up the asymptotics” by incorporating an estimated measure of skewness. I discuss how skewness-adjusted intervals can be computed for ratios, differences between domain means, and regression coefficients.
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