Relevant bibliographies by topics / Model Misspecification

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  2. Dissertations / Theses
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Academic literature on the topic 'Model Misspecification'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Model Misspecification"

1

Tarpey, Thaddeus, Dong Yun, and Eva Petkova. "Model misspecification." Statistical Modelling: An International Journal 8, no. 2 (2008): 199–218. http://dx.doi.org/10.1177/1471082x0800800204.

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Plümper, Thomas, and Vera E. Troeger. "Not so Harmless After All: The Fixed-Effects Model." Political Analysis 27, no. 1 (2018): 21–45. http://dx.doi.org/10.1017/pan.2018.17.

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The fixed-effects estimator is biased in the presence of dynamic misspecification and omitted within variation correlated with one of the regressors. We argue and demonstrate that fixed-effects estimates can amplify the bias from dynamic misspecification and that with omitted time-invariant variables and dynamic misspecifications, the fixed-effects estimator can be more biased than the ‘naïve’ OLS model. We also demonstrate that the Hausman test does not reliably identify the least biased estimator when time-invariant and time-varying omitted variables or dynamic misspecifications exist. Accor
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Yuan, Ke-Hai, Linda L. Marshall, and Peter M. Bentler. "8. Assessing the Effect of Model Misspecifications on Parameter Estimates in Structural Equation Models." Sociological Methodology 33, no. 1 (2003): 241–65. http://dx.doi.org/10.1111/j.0081-1750.2003.00132.x.

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Model misspecifications may have a systematic effect on parameters, causing biases in their estimates. In the application of structural equation models, every interesting model is fallible. When simultaneously evaluating a model, it is of interest to study whether all parameters are affected by a misspecification. This paper provides three procedures for evaluating such an effect: (1) analyzing the path, (2) using a functional relationship, and (3) using a significance test. Analyzing the path is illustrated through a confirmatory factor model. This method is ad hoc but intuitive. A more rigor
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Uppal, Raman, and Tan Wang. "Model Misspecification and Underdiversification." Journal of Finance 58, no. 6 (2003): 2465–86. http://dx.doi.org/10.1046/j.1540-6261.2003.00612.x.

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Winship, Christopher, and Bruce Western. "Multicollinearity and Model Misspecification." Sociological Science 3 (2016): 627–49. http://dx.doi.org/10.15195/v3.a27.

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Robitzsch, Alexander. "Modeling Model Misspecification in Structural Equation Models." Stats 6, no. 2 (2023): 689–705. http://dx.doi.org/10.3390/stats6020044.

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Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent. In many cases, researchers nevertheless intend to work with a misspecified target model of interest. In this article, a simultaneous statistical inference for sampling errors and model misspecification errors is discussed. A modified formula for the variance matrix of the parameter estimate is obtained by imposing a stochastic model for model errors and applying M-estimation theory. The presence of
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Bonhomme, Stéphane, and Martin Weidner. "Minimizing sensitivity to model misspecification." Quantitative Economics 13, no. 3 (2022): 907–54. http://dx.doi.org/10.3982/qe1930.

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We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systemati
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McMillen, Daniel P. "Spatial Autocorrelation Or Model Misspecification?" International Regional Science Review 26, no. 2 (2003): 208–17. http://dx.doi.org/10.1177/0160017602250977.

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Chien, Li-Chu, and Tsung-Shan Tsou. "Regression Diagnostic under Model Misspecification." Journal of Applied Statistics 34, no. 5 (2007): 563–75. http://dx.doi.org/10.1080/02664760701235014.

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Hansen, Lars Peter, Thomas J. Sargent, Gauhar Turmuhambetova, and Noah Williams. "Robust control and model misspecification." Journal of Economic Theory 128, no. 1 (2006): 45–90. http://dx.doi.org/10.1016/j.jet.2004.12.006.

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Dissertations / Theses on the topic "Model Misspecification"

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McCloud, Nadine. "Model misspecification theory and applications /." Diss., Online access via UMI:, 2008.

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Lu, Hung Yen Kevin. "Parametric interference under model misspecification." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530457.

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Sutherland, Sindee S. "Sequential design augmentation with model misspecification." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-10032007-171611/.

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Zhilova, Mayya. "Bootstrap confidence sets under model misspecification." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17380.

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Diese Arbeit befasst sich mit einem Multiplier-Bootstrap Verfahren für die Konstruktion von Likelihood-basierten Konfidenzbereichen in zwei verschiedenen Fällen. Im ersten Fall betrachten wir das Verfahren für ein einzelnes parametrisches Modell und im zweiten Fall erweitern wir die Methode, um Konfidenzbereiche für eine ganze Familie von parametrischen Modellen simultan zu schätzen. Theoretische Resultate zeigen die Validität der Bootstrap-Prozedur für eine potenziell begrenzte Anzahl an Beobachtungen, eine große Anzahl an betrachteten parametrischen Modellen, wachsende Parameterdimensionen u
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Kwong, Grace Pui Sze. "Model misspecification and random effect models in survival analysis." Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398729.

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Balshaw, Lloyd Stanley. "Model Misspecification and the Hedging of Exotic Options." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/28437.

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Asset pricing models are well established and have been used extensively by practitioners both for pricing options as well as for hedging them. Though Black-Scholes is the original and most commonly communicated asset pricing model, alternative asset pricing models which incorporate additional features have since been developed. We present three asset pricing models here - the Black-Scholes model, the Heston model and the Merton (1976) model. For each asset pricing model we test the hedge effectiveness of delta hedging, minimum variance hedging and static hedging, where appropriate. The option
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Sun, Yinghao. "Adaptive Design Optimization for Model Discrimination under Model Misspecification." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1347034580.

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Sias, Richard, Harry J. Turtle, and Blerina Zykaj. "Hedge Fund Return Dependence: Model Misspecification or Liquidity Spirals?" CAMBRIDGE UNIV PRESS, 2017. http://hdl.handle.net/10150/626073.

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We test whether model misspecification or liquidity spirals primarily explain the observed excess dependence in filtered (for economic fundamentals) hedge fund index returns and the links between volatility, liquidity shocks, and hedge fund return clustering. Evidence supports the model misspecification hypothesis: i) hedge fund filtered return clustering is symmetric, ii) filtered Short Bias fund returns exhibit negative dependence with filtered returns for other hedge fund types, iii) negative liquidity shocks are associated with clustering in both tails and market volatility subsumes the ro
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Monaghan, Paul Francis. "Model misspecification in survival analysis : applications to cancer research." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366244.

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Guastadisegni, Lucia <1993&gt. "Assessing the fit of unidimensional IRT models for binary data under model misspecification." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10304/1/PhD_thesis_GuastadisegniLucia.pdf.

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Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the
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Books on the topic "Model Misspecification"

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Coutts, J. Andrew. Misspecification of the market model: The implications for event studies. Sheffield University, School of Management, 1994.

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Bank, European Central, ed. Model misspecification, the equilibrium natural interest rate and the equity premium. European Central Bank, 2007.

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Mills, T. C. Misspecification testing and robust estimation of the market model and their implications for event studies. University of Hull. Department of Economics, 1994.

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Mills, T. C. Estimating betas for the FT-SE industry baskets: Misspecification testing and robust estimation of the market model. University of Hull, Department of Economics, 1993.

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Mills, Terence C. Misspecification testing and robust estimation of the market model: Estimating betas for the FT-SE industry baskets. Sheffield University, School of Management, 1994.

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Pollock, D. S. G. The misspecification of dynamic regression models. University of London. Queen Mary and Westfield College. Department of Economics, 1993.

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Pollock, S. The misspecification of dynamic regression models. London University, Queen Mary and WestfieldCollege, Department of Economics, 1993.

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Smith, Jeremy. Comparing the bias and misspecification in Arfima models. Warwick University, Department of Economics, 1995.

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Chen, Chen-Miao Carol. Examining Uncertainty and Misspecification of Attributes in Cognitive Diagnostic Models. [publisher not identified], 2013.

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Angrist, Joshua David. Quantile regression under misspecification, with an application to the U.S. wage structure. National Bureau of Economic Research, 2004.

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Book chapters on the topic "Model Misspecification"

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Zimmerman, Dale L. "Model Misspecification." In Linear Model Theory. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_12.

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Zimmerman, Dale L. "Model Misspecification." In Linear Model Theory. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8_12.

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Moosa, Imad A., and Kelly Burns. "Model Misspecification." In Demystifying the Meese-Rogoff Puzzle. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137452481_6.

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Huet, Sylvie, Annie Bouvier, Marie-Anne Gruet, and Emmanuel Jolivet. "Diagnostics of model misspecification." In Statistical Tools for Nonlinear Regression. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2523-0_4.

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Kato, Yuko, David M. J. Tax, and Marco Loog. "A View on Model Misspecification in Uncertainty Quantification." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39144-6_5.

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Huang, Xianzheng. "Dual Model Misspecification in Generalized Linear Models with Error in Variables." In New Developments in Statistical Modeling, Inference and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42571-9_1.

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Sahin, Sakine Gocer, Cindy M. Walker, and Selahattin Gelbal. "The Impact of Model Misspecification with Multidimensional Test Data." In Quantitative Psychology Research. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19977-1_12.

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Bozdogan, Hamparsum, J. Andrew Howe, Suman Katragadda, and Caterina Liberati. "Misspecification Resistant Model Selection Using Information Complexity with Applications." In Classification and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28894-4_20.

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Schmitt, Marvin, Paul-Christian Bürkner, Ullrich Köthe, and Stefan T. Radev. "Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54605-1_35.

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Bolfarine, Heleno, and Shelemyahu Zacks. "The Effects of Model Misspecification, Conditions for Robustness, and Bayesian Modeling." In Prediction Theory for Finite Populations. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2904-9_7.

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Conference papers on the topic "Model Misspecification"

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Lubeigt, Corentin. "Deliberate Model Misspecification for Weather Radar Signal Doppler Spectrum Moments Estimation." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715125.

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Mendrzik, Rico, and Gerhard Bauch. "Position Estimation under Model Misspecification." In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). IEEE, 2017. http://dx.doi.org/10.1109/vtcfall.2017.8288258.

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Wang, Lan. "Rank Regression under Possible Model Misspecification." In Nonparametric Statistics and Mixture Models - A Festschrift in Honor of Thomas P Hettmansperger. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814340564_0019.

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Masiha, Mohammad Saeed, Amin Gohari, Mohammad Hossein Yassaee, and Mohammad Reza Aref. "Learning under Distribution Mismatch and Model Misspecification." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517732.

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Jog, Varun, and Po-Ling Loh. "On model misspecification and KL separation for Gaussian graphical models." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282640.

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Mahaboob, B., S. Vijay Prasad, J. Peter Praveen, Ranadheer Donthi, and B. Venkateswarlu. "On misspecification tests for stochastic linear regression model." In RECENT TRENDS IN PURE AND APPLIED MATHEMATICS. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5135214.

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Li, Mingbing, Yijun Li, and Qun Wan. "Performance bound for Target Localization under Model Misspecification." In 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019. http://dx.doi.org/10.1109/iccc47050.2019.9064198.

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Mali, Siniša. "Model Misspecification as the Causes of Flypaper Effect." In International Scientific Conference on Digital Transformation in Business. MDPI, 2023. http://dx.doi.org/10.3390/proceedings2023085015.

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Richmond, Christ D. "On Constraints in Parameter Estimation and Model Misspecification." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455295.

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Ren, C., M. N. El Korso, J. Galy, E. Chaumette, P. Larzabal, and A. Renaux. "Performance bounds under misspecification model for MIMO radar application." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362436.

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Reports on the topic "Model Misspecification"

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Waggoner, Daniel, and Tao Zha. Confronting Model Misspecification in Macroeconomics. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w17791.

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Weidner, Martin, and Stéphane Bonhomme. Minimizing Sensitivity to Model Misspecification. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.3720.

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Weidner, Martin, and Stéphane Bonhomme. Minimizing sensitivity to model misspecification. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.5918.

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi та Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matric
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Information based inference in models with set-valued predictions and misspecification. Cemmap, 2024. http://dx.doi.org/10.47004/wp.cem.2024.0224.

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