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Journal articles on the topic 'Parameter estimability'

To see the other types of publications on this topic, follow the link: Parameter estimability.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Jacquez, John A., and Peter Greif. "Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design." Mathematical Biosciences 77, no. 1-2 (December 1985): 201–27. http://dx.doi.org/10.1016/0025-5564(85)90098-7.

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2

Dutta, Sandeep, and William F. Ebling. "Parameter Estimability of Biphasic Response Models." Journal of Pharmaceutical Sciences 86, no. 1 (January 1997): 44–51. http://dx.doi.org/10.1021/js960248f.

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3

Dansie, B. R. "Parameter estimability in the multinomial probit model." Transportation Research Part B: Methodological 19, no. 6 (December 1985): 526–28. http://dx.doi.org/10.1016/0191-2615(85)90047-5.

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4

Fries, R. H., and N. K. Cooperrider. "Bayesian Estimation of Transit Rail Vehicle Parameters." Journal of Dynamic Systems, Measurement, and Control 107, no. 2 (June 1, 1985): 151–58. http://dx.doi.org/10.1115/1.3149687.

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Bayesian parameter estimation is a powerful and versatile method with good convergence properties. This paper describes the use of the Bayesian method to estimate the parameters of a transit rail vehicle. The paper also addresses several issues of general interest in estimation work: cost function formulation, signal processing methods, parameter linking, and parameter estimability.

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5

Fu, Caihong, and Terrance J. Quinn II. "Estimability of natural mortality and other population parameters in a length-based model: Pandalus borealis in Kachemak Bay, Alaska." Canadian Journal of Fisheries and Aquatic Sciences 57, no. 12 (December 1, 2000): 2420–32. http://dx.doi.org/10.1139/f00-220.

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Because stock-assessment models have become more complex, the question of estimability of population parameters is important. We conducted simulation-estimation experiments with a length-based model of the pink shrimp (Pandalus borealis Krøyer) population in Kachemak Bay, Alaska, to compare the robustness of various modeling approaches to specifications of natural mortality (M), catchability (q), a survey gear selectivity parameter (L50), and data imprecision. Our goal was to determine the estimability of various parameters, particularly M, q, and L50. Simulation results suggest that interannual variations in M and L50 could be estimated, if the underlying values have a trend over time. However, parameter q should be fixed at a chosen value, even when it varies over time. Estimated M for the Kachemak Bay P. borealis population increased in the 1980s, perhaps owing to increased predation mortality by groundfish.

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6

Peacock, Stephanie Jane, Martin Krkošek, Mark Alun Lewis, and Subhash Lele. "Study design and parameter estimability for spatial and temporal ecological models." Ecology and Evolution 7, no. 2 (December 30, 2016): 762–70. http://dx.doi.org/10.1002/ece3.2618.

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7

Nogueira, Idelfonso B. R., and Karen V. Pontes. "Parameter estimation with estimability analysis applied to an industrial scale polymerization process." Computers & Chemical Engineering 96 (January 2017): 75–86. http://dx.doi.org/10.1016/j.compchemeng.2016.10.013.

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8

Jiang, Bo, Ming Liu, and Yongge Tian. "On two correlated linear models with common and different parameters." Mathematica Slovaca 72, no. 5 (October 1, 2022): 1355–74. http://dx.doi.org/10.1515/ms-2022-0092.

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Abstract A parametric regression model may comprise several correlated individual regression equations, and these equations may have common and different unknown parameters. In such a situation, the common unknown parameter vectors in these equations can be estimated individually or simultaneously according to various available statistical inference methods. The purpose of this paper is to provide an integral account of two classic objects in regression theory: the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) on common and different unknown parameters in two correlated linear models with common and different parameters. We first introduce some reduced models associated with the two correlated linear models. We then define and characterize predictability and estimability of all unknown parameter vectors in the two correlated models and their reduced models, and derive analytical formulas for calculating the BLUPs and BLUEs of all unknown parameter vectors in these models by means of a constrained quadratic matrix optimization method. We also discuss a variety of theoretical properties of the predictors and estimators.

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9

Bouchkira, Ilias, Abderrazak M. Latifi, Lhachmi Khamar, and Saad Benjelloun. "Global sensitivity based estimability analysis for the parameter identification of Pitzer’s thermodynamic model." Reliability Engineering & System Safety 207 (March 2021): 107263. http://dx.doi.org/10.1016/j.ress.2020.107263.

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10

Yao, K. Zhen, Benjamin M. Shaw, Bo Kou, Kim B. McAuley, and D. W. Bacon. "Modeling Ethylene/Butene Copolymerization with Multi‐site Catalysts: Parameter Estimability and Experimental Design." Polymer Reaction Engineering 11, no. 3 (January 9, 2003): 563–88. http://dx.doi.org/10.1081/pre-120024426.

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11

Mao, Lu. "A unified approach to the calculation of information operators in semiparametric models." Biometrika 107, no. 4 (June 14, 2020): 983–95. http://dx.doi.org/10.1093/biomet/asaa037.

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Summary The infinite-dimensional information operator for the nuisance parameter plays a key role in semiparametric inference, as it is closely related to the regular estimability of the target parameter. Calculation of information operators has traditionally proceeded in a case-by-case manner and has often entailed lengthy derivations with complicated arguments. We develop a unified framework for this task by exploiting commonality in the form of semiparametric likelihoods. The general formula developed allows one to derive information operators with simple calculus and, if necessary at all, a minimal amount of probabilistic evaluation. This streamlined approach shows its simplicity and versatility in application to a number of existing models as well as a new model of practical interest.

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12

Wu, Shaohua, Kevin A. P. McLean, Thomas J. Harris, and Kimberley B. McAuley. "Selection of optimal parameter set using estimability analysis and MSE-based model-selection criterion." International Journal of Advanced Mechatronic Systems 3, no. 3 (2011): 188. http://dx.doi.org/10.1504/ijamechs.2011.042615.

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13

Weeks, Melvyn. "The Multinomial Probit Model Revisited: A Discussion of Parameter Estimability, Identification and Specification Testing." Journal of Economic Surveys 11, no. 3 (September 1997): 297–320. http://dx.doi.org/10.1111/1467-6419.00035.

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14

Haslett, Stephen. "Degrees of freedom and parameter estimability in hierarchical models for sparse complete contingency tables." Computational Statistics & Data Analysis 9, no. 2 (March 1990): 179–95. http://dx.doi.org/10.1016/0167-9473(90)90118-2.

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15

MORRIS, SARA R., AMANDA M. LARRACUENTE, KRISTEN M. COVINO, MELISSA S. MUSTILLO, KATHRYN E. MATTERN, DAVID A. LIEBNER, and H. DAVID SHEETS. "UTILITY OF OPEN POPULATION MODELS: LIMITATIONS POSED BY PARAMETER ESTIMABILITY IN THE STUDY OF MIGRATORY STOPOVER." Wilson Journal of Ornithology 118, no. 4 (December 2006): 513–26. http://dx.doi.org/10.1676/05-073.1.

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16

Zhang, Chen, Zhijiang Shao, Xi Chen, Zhen Yao, Xueping Gu, and Lorenz T. Biegler. "Kinetic parameter estimation of HDPE slurry process from molecular weight distribution: Estimability analysis and multistep methodology." AIChE Journal 60, no. 10 (June 24, 2014): 3442–59. http://dx.doi.org/10.1002/aic.14527.

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17

PARIS, MATTEO G. A. "QUANTUM ESTIMATION FOR QUANTUM TECHNOLOGY." International Journal of Quantum Information 07, supp01 (January 2009): 125–37. http://dx.doi.org/10.1142/s0219749909004839.

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Several quantities of interest in quantum information, including entanglement and purity, are nonlinear functions of the density matrix and cannot, even in principle, correspond to proper quantum observables. Any method aimed to determine the value of these quantities should resort to indirect measurements and thus corresponds to a parameter estimation problem whose solution, i.e. the determination of the most precise estimator, unavoidably involves an optimization procedure. We review local quantum estimation theory and present explicit formulas for the symmetric logarithmic derivative and the quantum Fisher information of relevant families of quantum states. Estimability of a parameter is defined in terms of the quantum signal-to-noise ratio and the number of measurements needed to achieve a given relative error. The connections between the optmization procedure and the geometry of quantum statistical models are discussed. Our analysis allows to quantify quantum noise in the measurements of non observable quantities and provides a tools for the characterization of signals and devices in quantum technology.

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18

Campbell, David, and Subhash Lele. "An ANOVA test for parameter estimability using data cloning with application to statistical inference for dynamic systems." Computational Statistics & Data Analysis 70 (February 2014): 257–67. http://dx.doi.org/10.1016/j.csda.2013.09.013.

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19

Trenkel, Verena M., Mark V. Bravington, and Pascal Lorance. "A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality." Canadian Journal of Fisheries and Aquatic Sciences 69, no. 11 (November 2012): 1881–93. http://dx.doi.org/10.1139/f2012-103.

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Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

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20

Ferreira, Johannes, and Ané van der Merwe. "A Noncentral Lindley Construction Illustrated in an INAR(1) Environment." Stats 5, no. 1 (January 10, 2022): 70–88. http://dx.doi.org/10.3390/stats5010005.

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This paper proposes a previously unconsidered generalization of the Lindley distribution by allowing for a measure of noncentrality. Essential structural characteristics are investigated and derived in explicit and tractable forms, and the estimability of the model is illustrated via the fit of this developed model to real data. Subsequently, this model is used as a candidate for the parameter of a Poisson model, which allows for departure from the usual equidispersion restriction that the Poisson offers when modelling count data. This Poisson-noncentral Lindley is also systematically investigated and characteristics are derived. The value of this count model is illustrated and implemented as the count error distribution in an integer autoregressive environment, and juxtaposed against other popular models. The effect of the systematically-induced noncentrality parameter is illustrated and paves the way for future flexible modelling not only as a standalone contender in continuous Lindley-type scenarios but also in discrete and discrete time series scenarios when the often-encountered equidispersed assumption is not adhered to in practical data environments.

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21

McLean, Kevin A. P., and Kim B. McAuley. "Mathematical modelling of chemical processes-obtaining the best model predictions and parameter estimates using identifiability and estimability procedures." Canadian Journal of Chemical Engineering 90, no. 2 (October 17, 2011): 351–66. http://dx.doi.org/10.1002/cjce.20660.

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22

Takwoingi, Yemisi, Boliang Guo, Richard D. Riley, and Jonathan J. Deeks. "Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data." Statistical Methods in Medical Research 26, no. 4 (June 26, 2015): 1896–911. http://dx.doi.org/10.1177/0962280215592269.

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Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies. Performance of the models was assessed in terms of estimability (percentage of meta-analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted. Alternatively, an HSROC model that assumes a symmetric SROC curve (by excluding the shape parameter) can be used if the HSROC model is the chosen meta-analytic approach. In the absence of heterogeneity, fixed effect equivalent of the models can be applied.

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23

Kung’u, Ngigi Peter, J. K. Arap Koske, and Josphat K. Kinyanjui. "A- Optimal Slope Design for Second Degree Kronecker Model Mixture Experiment With Four Ingredients With Application in Selected Fruits Blending." International Journal of Statistics and Probability 10, no. 2 (February 2, 2021): 36. http://dx.doi.org/10.5539/ijsp.v10n2p36.

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This study presents an investigation of an optimal slope design in the second degree Kronecker model for mixture experiments in four dimensions and its application in blending of selected fruits to prepare punch. The study centers around weighted centroid designs, with the second degree Kronecker model. This is guided by the fact that the class of weighted centroid designs is a complete class in the Kiefer Ordering. To overcome the problem of estimability, a concise coefficient matrix is defined that aid in selecting a maximal parameter subsystem for the Kronecker model. The information matrix of the design is obtained using a linear function of the moment matrices for the centroids and directly linked to the slope matrix. The discussion is based on Kronecker product algebra which clearly reflects the symmetries of the simplex experimental region. From the family of matrix means, a well-defined function is used to determine optimal values of the efficient developed design. Finally, a demonstration is provided for the case where the design is applied in fruit blending.

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24

Wagner, David, and Wolfgang Schlüter. "Model-Based Computation of Critical Operation Points in Biogas Producing Plants." Applied Mechanics and Materials 882 (July 2018): 45–52. http://dx.doi.org/10.4028/www.scientific.net/amm.882.45.

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Reducing the use of fossil fuels for energy production is one of the main objectives in 21st century. In order to achieve this target renewable energy resources (like agricultural waste) in biogas plants can be used. An anaerobic bacterial fermentation process digests the substrates into methane and carbon dioxide. The process itself has strong fluctuations in terms of net methane yield due to different amounts and composition of agricultural influents. For increasing the space time yield two main difficulties are encountered. The first one is system-specific and includes stirrer design and reactor geometry. The second affects the biotechnological fermentation process. The following work is focusing on the fermentation process. The determination of critical parameters for the optimization of the anaerobic microbial digestion is investigated. An economic approach for solving these problems is only feasible by using mathematical models and simulation. Consequently two fermentation models are compared by regarding parameter sensitivity and critical operational points. The first one is based on simple Monod-kinetics while the second one is extended with two steps of fermentation and therefore two different microbial consortia and additive inhibition effects. The complex model is able to describe different phenomena in more detail. But its estimability and therefore its validation is difficult without further investigation of the model structure and the reduction of the model complexity. One important result of the investigation is that stable process conditions with simultaneous high yields are depending on a careful adjustment of the loading rate and therefore requiring precise model parameters.

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25

Limberger, M., W. Liang, M. Schmidt, D. Dettmering, M. Hernández-Pajares, and U. Hugentobler. "Correlation studies for B-spline modeled F2 Chapman parameters obtained from FORMOSAT-3/COSMIC data." Annales Geophysicae 32, no. 12 (December 17, 2014): 1533–45. http://dx.doi.org/10.5194/angeo-32-1533-2014.

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Abstract. The determination of ionospheric key quantities such as the maximum electron density of the F2 layer NmF2, the corresponding F2 peak height hmF2 and the F2 scale height HF2 are of high relevance in 4-D ionosphere modeling to provide information on the vertical structure of the electron density (Ne). The Ne distribution with respect to height can, for instance, be modeled by the commonly accepted F2 Chapman layer. An adequate and observation driven description of the vertical Ne variation can be obtained from electron density profiles (EDPs) derived by ionospheric radio occultation measurements between GPS and low Earth orbiter (LEO) satellites. For these purposes, the six FORMOSAT-3/COSMIC (F3/C) satellites provide an excellent opportunity to collect EDPs that cover most of the ionospheric region, in particular the F2 layer. For the contents of this paper, F3/C EDPs have been exploited to determine NmF2, hmF2 and HF2 within a regional modeling approach. As mathematical base functions, endpoint-interpolating polynomial B-splines are considered to model the key parameters with respect to longitude, latitude and time. The description of deterministic processes and the verification of this modeling approach have been published previously in Limberger et al. (2013), whereas this paper should be considered as an extension dealing with related correlation studies, a topic to which less attention has been paid in the literature. Relations between the B-spline series coefficients regarding specific key parameters as well as dependencies between the three F2 Chapman key parameters are in the main focus. Dependencies are interpreted from the post-derived correlation matrices as a result of (1) a simulated scenario without data gaps by taking dense, homogenously distributed profiles into account and (2) two real data scenarios on 1 July 2008 and 1 July 2012 including sparsely, inhomogeneously distributed F3/C EDPs. Moderate correlations between hmF2 and HF2 as well as inverse correlations between NmF2 and HF2 are reflected from the simulation. By means of the real data studies, it becomes obvious that the sparse measurement distribution leads to an increased weighting of the prior information and suppresses the parameter correlations which play an important role regarding the parameter estimability. The currently implemented stochastic model is in need of improvement and does not consider stochastic correlations which consequently cannot occur.

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26

Kounias, Stratis, and Miltiadis Chalikias. "Estimability of parameters in a linear model and related characterizations." Statistics & Probability Letters 78, no. 15 (October 2008): 2437–39. http://dx.doi.org/10.1016/j.spl.2008.02.019.

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27

Zaharatos, Brian R., Mark Campanelli, and Luis Tenorio. "On the estimability of the PV single-diode model parameters." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 5-6 (August 14, 2015): 329–39. http://dx.doi.org/10.1002/sam.11286.

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28

Ngo, Viet V., Abderrazak Latifi, and Marie-Odile Simonnot. "Soil Hydraulic Parameters Characterizing Preferential Water Flow: Estimability Analysis and Identification." Journal of Hydrologic Engineering 19, no. 10 (October 2014): 04014017. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000953.

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29

Bedel, S., C. Vallières, and M. A. Latifi. "Parameters estimability analysis and identification for adsorption equilibrium models of carbon dioxide." Adsorption 23, no. 2-3 (February 6, 2017): 373–80. http://dx.doi.org/10.1007/s10450-017-9864-7.

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30

Ngo, V. V., M. A. Latifi, and M. O. Simonnot. "Estimability Analysis and Optimisation of Soil Hydraulic Parameters from Field Lysimeter Data." Transport in Porous Media 98, no. 2 (April 9, 2013): 485–504. http://dx.doi.org/10.1007/s11242-013-0155-9.

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31

Rikimaru, Yuuki, and Ritei Shibata. "Non-Identifiability of Simultaneous Spatial Autoregressive Model and Singularity of Fisher Information Matrix." International Journal of Statistics and Probability 5, no. 4 (June 15, 2017): 31. http://dx.doi.org/10.5539/ijsp.v6n4p31.

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Simultaneous spatial autoregressive model is widely used for spatial data analysis, observed at a set of grid points in a space. However a problem, not so well known, is that there exists no unique model unlike time series AR model for given autocovariances or spectral density. We show that such a non-identifiability of the model implies existence of multiple maximum likelihood estimates under Gaussianity and causes non-estimability of parameters and the singularity of Fisher information matrix. Several types of necessary and sufficient conditions for the singularity are given.

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32

Christopeit, Norbert, and Michael Massmann. "ESTIMATING STRUCTURAL PARAMETERS IN REGRESSION MODELS WITH ADAPTIVE LEARNING." Econometric Theory 34, no. 1 (January 9, 2017): 68–111. http://dx.doi.org/10.1017/s0266466616000529.

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This paper examines the ordinary least squares (OLS) estimator of the structural parameters in a simple macroeconomic model in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous variable. The popularity of learning models has recently increased amongst applied economists and policy makers who seek to estimate them empirically. Yet the econometrics of learning models is largely uncharted territory. We consider two prominent learning algorithms, namely constant gain and decreasing gain learning. For each of the two learning rules, our analysis proceeds in two stages. First, the paper derives the asymptotic properties of agents’ expectations. At the second stage, the paper derives the asymptotics of OLS in the structural model, taking the first stage learning dynamics as given. In the case of constant gain learning, the structural model effectively amounts to a stationary, cointegrating, or co-explosiveness regression. With decreasing gain learning, the regressors are asymptotically collinear such that OLS does not satisfy, in general, the Grenander conditions for consistent estimability. Nevertheless, this paper shows that the OLS estimator remains consistent in all models considered. It also shows, however, that its asymptotic distribution, and hence any inference based upon it, may be nonstandard.

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33

Hyun, Saang-Yoon, and Kyuhan Kim. "An evaluation of estimability of parameters in the state-space non-linear logistic production model." Fisheries Research 245 (January 2022): 106135. http://dx.doi.org/10.1016/j.fishres.2021.106135.

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34

Adkison, Milo D., and Zhenming Su. "A comparison of salmon escapement estimates using a hierarchical Bayesian approach versus separate maximum likelihood estimation of each year's return." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 8 (August 1, 2001): 1663–71. http://dx.doi.org/10.1139/f01-100.

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In this simulation study, we compared the performance of a hierarchical Bayesian approach for estimating salmon escapement from count data with that of separate maximum likelihood estimation of each year's escapement. We simulated several contrasting counting schedules resulting in data sets that differed in information content. In particular, we were interested in the ability of the Bayesian approach to estimate escapement and timing in years where few or no counts are made after the peak of escapement. We found that the Bayesian hierarchical approach was much better able to estimate escapement and escapement timing in these situations. Separate estimates for such years could be wildly inaccurate. However, even a single postpeak count could dramatically improve the estimability of escapement parameters.

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35

Lei, M., C. Vallieres, G. Grevillot, and M. A. Latifi. "Thermal Swing Adsorption Process for Carbon Dioxide Capture and Recovery: Modeling, Simulation, Parameters Estimability, and Identification." Industrial & Engineering Chemistry Research 52, no. 22 (May 20, 2013): 7526–33. http://dx.doi.org/10.1021/ie3029152.

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36

Ngo, Viet V., Horst H. Gerke, and Annika Badorreck. "Estimability Analysis for Optimization of Hysteretic Soil Hydraulic Parameters Using Data of a Field Irrigation Experiment." Transport in Porous Media 103, no. 3 (May 6, 2014): 535–62. http://dx.doi.org/10.1007/s11242-014-0315-6.

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37

Ngo, Viet V., Julien Michel, Valérie Gujisaite, Abderrazak Latifi, and Marie-Odile Simonnot. "Parameters describing nonequilibrium transport of polycyclic aromatic hydrocarbons through contaminated soil columns: Estimability analysis, correlation, and optimization." Journal of Contaminant Hydrology 158 (March 2014): 93–109. http://dx.doi.org/10.1016/j.jconhyd.2014.01.005.

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38

Ngo, Viet V., Julien Michel, Lise Lucas, Abderrazak Latifi, and Marie-Odile Simonnot. "Sensitivity, estimability and correlation of parameters describing equilibrium and nonequilibrium transports of bromide tracer in the field lysimeter." European Journal of Environmental and Civil Engineering 19, no. 4 (August 26, 2014): 445–66. http://dx.doi.org/10.1080/19648189.2014.950759.

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39

Sólymos, Péter, Steven M. Matsuoka, Steven G. Cumming, Diana Stralberg, Patricia Fontaine, Fiona K. A. Schmiegelow, Samantha J. Song, and Erin M. Bayne. "Evaluating time-removal models for estimating availability of boreal birds during point count surveys: Sample size requirements and model complexity." Condor 120, no. 4 (August 29, 2018): 765–86. http://dx.doi.org/10.1650/condor-18-32.1.

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Abstract We used conventional and finite mixture removal models with and without time-varying covariates to evaluate availability given presence for 152 bird species using data from point counts in boreal North America. We found that the choice of model had an impact on the estimability of unknown model parameters and affected the bias and variance of corrected counts. Finite mixture models provided better fit than conventional removal models and better adjusted for count duration. However, reliably estimating parameters and minimizing variance using mixture models required at least 200–1,000 detections. Mixture models with time-varying proportions of infrequent singers were best supported across species, indicating that accounting for date- and time-related heterogeneity is important when combining data across studies over large spatial scales, multiple sampling time frames, or variable survey protocols. Our flexible and continuous time-removal modeling framework can be used to account for such heterogeneity through the incorporation of easily obtainable covariates, such as methods, date, time, and location. Accounting for availability bias in bird surveys allows for better integration of disparate studies at large spatial scales and better adjustment of local, regional, and continental population size estimates.

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40

Benyahia, B., M. A. Latifi, C. Fonteix, and F. Pla. "Emulsion copolymerization of styrene and butyl acrylate in the presence of a chain transfer agent. Part 2: Parameters estimability and confidence regions." Chemical Engineering Science 90 (March 2013): 110–18. http://dx.doi.org/10.1016/j.ces.2012.12.013.

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41

Odijk, Dennis, Baocheng Zhang, Amir Khodabandeh, Robert Odolinski, and Peter J. G. Teunissen. "On the estimability of parameters in undifferenced, uncombined GNSS network and PPP-RTK user models by means of $$\mathcal {S}$$ S -system theory." Journal of Geodesy 90, no. 1 (November 5, 2015): 15–44. http://dx.doi.org/10.1007/s00190-015-0854-9.

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42

Yagyaev, Elmar. "Determining deviations of output variables from forecast values in system for monitoring external round grinding process." MATEC Web of Conferences 224 (2018): 01127. http://dx.doi.org/10.1051/matecconf/201822401127.

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The article considers the estimability of the deviations of actual output variable values from the forecast ones in the system for monitoring the process of external round grinding. The author proposes the method for determining the deviations and establishing their most probable values as well as for establishing the cause of such output variable value deviations and technological system changes. It is substantiated that the determination of the output variable deviations and the parameters of the technological system state is possible at the control of the output variable change rate as the interruption time intervals are different for different subsystems. The authors managed to determine the dependences of the deviations of actual values (t) from the estimated ones yр(t). To resolve the objective of identifying the causes of output value deviations from the forecast ones, the authors applied the method for determining the indirect measurement errors. The obtained dependences made the authors draw the conclusion that at the control of the change rate of output variable values one is able to find out the disturbance type and the type of the deviation of output variable actual values in the technical system. This provides for the opportunity to correct the control of the external round grinding process as well as to calculate the time intervals at which one needs to take into account some or other changes in the technological system.

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43

Chatzinikos, Miltiadis, and Athanasios Dermanis. "A coordinate-invariant model for deforming geodetic networks: understanding rank deficiencies, non-estimability of parameters, and the effect of the choice of minimal constraints." Journal of Geodesy 91, no. 4 (November 30, 2016): 375–96. http://dx.doi.org/10.1007/s00190-016-0970-1.

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44

Ahlborn, W., H. J. Tuz, and K. Überla. "Estimating Relative Risk from Heterogeneous Strata." Methods of Information in Medicine 27, no. 03 (July 1988): 118–24. http://dx.doi.org/10.1055/s-0038-1635527.

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SummaryThe uncritical use of risk estimators can lead to serious bias. The estimation of overall risk ratios for heterogeneous strata and the weights used have been discussed recently. This paper starts with the definition of relative risk in the population, considering heterogeneous strata and a cofactor, using weight functions. From this general formula four different weight functions leading to four different overall measures of relative risk (RR1, RR1, RR3, RR4 ) are. derived as special cases. The estimability of the parameters in relation to the underlying sampling design is investigated. Consistent estimators are provided and the asymptotic distributions of the estimators are given for prospective cohort studies, case control studies with fixed strata and case control studies with given sample sizes. For some of the estimates the . asymptotic variances are given. It is shown that for case control studies with fixed strata neither the Mantel-Haenszel estimator nor another estimator of this class is consistent. An extension to more than two risk levels is provided and the relationships between various risk measures and other implications are commented on. A way is described to improve risk estimation by using information from the population. An example for cohort studies is given. Generally we propose to use RR3 , which lies between RR1 and RR2 and can be estimated more precisely using the sometimes available distribution of the cofactor in the population. If the relative risk across strata is heterogeneous, logistic models for the construction of a single risk indicator have some drawbacks.

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Souflas, Ioannis, Antonios Pezouvanis, and Kambiz M. Ebrahimi. "Nonlinear Recursive Estimation With Estimability Analysis for Physical and Semiphysical Engine Model Parameters." Journal of Dynamic Systems, Measurement, and Control 138, no. 2 (December 11, 2015). http://dx.doi.org/10.1115/1.4032052.

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A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semiphysical engine models is presented. Orthogonal estimability analysis based on parameter sensitivity is employed with the purpose of evaluating a rank of estimable parameters given multiple sets of observation data that were acquired from a transient engine testing facility. The qualitative information gained from the estimability analysis is then used for estimating the estimable parameters by using two well-known nonlinear adaptive estimation algorithms known as extended Kalman filter (EKF) and unscented Kalman filter (UKF). The findings of this work contribute on understanding the real-world challenges which are involved in the effective implementation of system identification techniques suitable for online nonlinear estimation of parameters with physical interpretation.

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Ferguson, Jake M., Mark L. Taper, Rosana Zenil-Ferguson, Marie Jasieniuk, and Bruce D. Maxwell. "Incorporating Parameter Estimability Into Model Selection." Frontiers in Ecology and Evolution 7 (November 14, 2019). http://dx.doi.org/10.3389/fevo.2019.00427.

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47

Liu, Fei F., Anh‐Duong D. Vo, and Kimberley B. McAuley. "Diagnosing Parameter Estimability Problems in Polymerization Models." Macromolecular Theory and Simulations, August 18, 2021, 2100045. http://dx.doi.org/10.1002/mats.202100045.

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48

Bouchkira, Ilias, Saad Benjelloun, Lhachmi Khamar, and Abderrazak M. Latifi. "Thermodynamic modeling and parameter estimability analysis of a wet phosphoric acid process with impurities." Fluid Phase Equilibria, September 2022, 113594. http://dx.doi.org/10.1016/j.fluid.2022.113594.

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49

Wilk, Agata Małgorzata, Krzysztof Łakomiec, Krzysztof Psiuk-Maksymowicz, and Krzysztof Fujarewicz. "Impact of government policies on the COVID-19 pandemic unraveled by mathematical modelling." Scientific Reports 12, no. 1 (October 10, 2022). http://dx.doi.org/10.1038/s41598-022-21126-2.

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AbstractSince the very beginning of the COVID-19 pandemic, control policies and restrictions have been the hope for containing the rapid spread of the virus. However, the psychological and economic toll they take on society entails the necessity to develop an optimal control strategy. Assessment of the effectiveness of these interventions aided with mathematical modelling remains a non-trivial issue in terms of numerical conditioning due to the high number of parameters to estimate from a highly noisy dataset and significant correlations between policy timings. We propose a solution to the problem of parameter non-estimability utilizing data from a set of European countries. Treating a subset of parameters as common for all countries and the rest as country-specific, we construct a set of individualized models incorporating 13 different pandemic control measures, and estimate their parameters without prior assumptions. We demonstrate high predictive abilities of these models on an independent validation set and rank the policies by their effectiveness in reducing transmission rates. We show that raising awareness through information campaigns, providing income support, closing schools and workplaces, cancelling public events, and maintaining an open testing policy have the highest potential to mitigate the pandemic.

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Schumann-Bischoff, J., S. Luther, and U. Parlitz. "Estimability and dependency analysis of model parameters based on delay coordinates." Physical Review E 94, no. 3 (September 28, 2016). http://dx.doi.org/10.1103/physreve.94.032221.

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