Relevant bibliographies by topics / Parameter estimates

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  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: 15 June 2025

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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 (2013): 5210–23. http://dx.doi.org/10.1016/j.amc.2012.11.067.

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Robitzsch, Alexander. "Linking Error Estimation in Fixed Item Parameter Calibration: Theory and Application in Large-Scale Assessment Studies." Foundations 5, no. 1 (2025): 4. https://doi.org/10.3390/foundations5010004.

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In fixed item parameter calibration (FIPC), an item response theory (IRT) model is estimated with item parameters fixed at reference values to estimate the distribution parameters within a specific group. The presence of random differential item functioning (DIF) within this group introduces additional variability in the distribution parameter estimates, which is captured by the linking error (LE). Conventional LE estimates, based on item jackknife methods, are subject to positive bias due to sampling errors. To address this, this article introduces a bias-corrected LE estimate. Moreover, the use of statistical inference is examined using the newly proposed bias-corrected total error, which includes both the sampling error and LE. The proposed error estimates were evaluated through a simulation study, and their application is illustrated using PISA 2006 data for the reading domain.
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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 (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 (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 (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 (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 (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 (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 (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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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 (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.<br>Doctor of Philosophy<br>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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 167-169).<br>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.<br>by Zuoyu Tao.<br>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. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1993.

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L, McDonald, ed. Estimation and analysis of insect populations: Proceedings of a conference held in Laramie, Wyoming, January 25-29, 1988. Springer-Verlag, 1989.

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

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Vladislav, Klein, and Langley Research Center, eds. Determining the accuracy of maximum likelihood parameter estimates with colored residuals. National Aeronautics and Space Administration, Langley Research Center, 1994.

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

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Glas, Cees A. W. Alternative approaches to updating item parameter estimates in tests with item cloning. 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. University of Illinois at Urbana-Champaign, 1992.

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

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United States. National Aeronautics and Space Administration., ed. Lower bound on reliability for Weibull distribution when shape parameter is not estimated accurately. National Aeronautics and Space Administration, 1990.

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

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Book chapters on the topic "Parameter estimates"

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Ritgen, Ulf. "Parameter Estimates." In Analytical Chemistry II. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-68710-9_21.

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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. 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. 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. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_3657.

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Kalmijn, Wim. "Happiness Population Distribution Parameter Estimates." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_3657.

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Komornik, Vilmos. "Decay Estimates for the Wave Equation." In Control and Optimal Design of Distributed Parameter Systems. 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. 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. 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. 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. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1072-3_13.

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Conference papers on the topic "Parameter estimates"

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Sørensen, Kristian Aa, Constantin Günzel, Hasse Pedersen, Peder Heiselberg, and Henning Heiselberg. "3D Ship Parameter Estimates in SAR Imagery." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640640.

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Avilés, Esteban, Roberto Lavarello, and Andres Coila. "Nonlinearity parameter imaging of local estimates using spatial compounding." In 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS). IEEE, 2024. https://doi.org/10.1109/uffc-js60046.2024.10793740.

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Wellander, Niklas, Olof Lundén, and Mats Bäckström. "Parameter Estimates for the Stirrer Efficiency in Reverberation Chambers." In 16th International Zurich Symposium and Technical Exposition on Electromagnetic Compatibility. IEEE, 2005. https://doi.org/10.23919/emc.2005.10806096.

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Haitz, Magnus, Martin Richter, and Ingmar Kallfass. "Robustness across Initial Estimates of Optimization Algorithms for Power Semiconductor Model Parameter Extraction." In 2024 IEEE 11th Workshop on Wide Bandgap Power Devices & Applications (WiPDA). IEEE, 2024. https://doi.org/10.1109/wipda62103.2024.10773067.

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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. 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. 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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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. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20673.

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Powell, Frederic D. Effects of Parameter Uncertainties on Software Development Effort Estimates. Defense Technical Information Center, 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), 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. Iowa State University, 2008. http://dx.doi.org/10.31274/ans_air-180814-64.

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Villoria, Nelson B., and Jing Liu. Using continental grids to improve our understanding of global land supply responses: Implications for policy-driven land use changes in the Americas. GTAP Working Paper, 2015. http://dx.doi.org/10.21642/gtap.wp81.

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Global economic models with explicit treatment of global land markets are crucial to understanding the consequences of different policy choices on global food and environmental security. However, these models rely on parameters for which there is little econometric evidence. A fundamental parameter in these models is the land supply elasticity. We provide a novel set of land supply elasticities estimated using gridded data for the American continent, and we use them in exploring previous work on the indirect land-use effects of US ethanol policy. Our estimates provide a basis for better-informed simulations of global land-use transitions under different economic and policy scenarios. JEL Codes: Q24, C21, C68
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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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Iowa State University, 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, 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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Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, 2003. http://dx.doi.org/10.21642/gtap.wp26.

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With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) Ho
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