Gotowe bibliografie tematyczne / Variance model

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Variance model”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 1 lutego 2022

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Artykuły w czasopismach na temat "Variance model"

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Kubáček, Lubomír. "Linear model with inaccurate variance components." Applications of Mathematics 41, no. 6 (1996): 433–45. http://dx.doi.org/10.21136/am.1996.134336.

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Volaufová, Júlia. "On variance of the two-stage estimator in variance-covariance components model." Applications of Mathematics 38, no. 1 (1993): 1–9. http://dx.doi.org/10.21136/am.1993.104529.

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Pardavi-Horvath, M., E. Della Torre, and F. Vajda. "A variable variance Preisach model (garnet film)." IEEE Transactions on Magnetics 29, no. 6 (November 1993): 3793–95. http://dx.doi.org/10.1109/20.281302.

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Zainodin, H. J., G. Khuneswari, A. Noraini, and F. A. A. Haider. "Selected Model Systematic Sequence via Variance Inflationary Factor." International Journal of Applied Physics and Mathematics 5, no. 2 (2015): 105–14. http://dx.doi.org/10.17706/ijapm.2015.5.2.105-114.

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Bishop, Craig H., and Elizabeth A. Satterfield. "Hidden Error Variance Theory. Part I: Exposition and Analytic Model." Monthly Weather Review 141, no. 5 (May 1, 2013): 1454–68. http://dx.doi.org/10.1175/mwr-d-12-00118.1.

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Abstract A conundrum of predictability research is that while the prediction of flow-dependent error distributions is one of its main foci, chaos fundamentally hides flow-dependent forecast error distributions from empirical observation. Empirical estimation of such error distributions requires a large sample of error realizations given the same flow-dependent conditions. However, chaotic elements of the flow and the observing network make it impossible to collect a large enough conditioned error sample to empirically define such distributions and their variance. Such conditional variances are
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Borcia, I. D., L. Spinu, and A. Stancu. "A Preisach-Neel model with thermally variable variance." IEEE Transactions on Magnetics 38, no. 5 (September 2002): 2415–17. http://dx.doi.org/10.1109/tmag.2002.803611.

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Hjalmarsson, H. "A Model Variance Estimator." IFAC Proceedings Volumes 26, no. 2 (July 1993): 335–40. http://dx.doi.org/10.1016/s1474-6670(17)49139-7.

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Koul, Hira L., and Weixing Song. "Conditional variance model checking." Journal of Statistical Planning and Inference 140, no. 4 (April 2010): 1056–72. http://dx.doi.org/10.1016/j.jspi.2009.10.008.

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Stuchlý, Jaroslav. "Bayes unbiased estimation in a model with two variance components." Applications of Mathematics 32, no. 2 (1987): 120–30. http://dx.doi.org/10.21136/am.1987.104241.

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Stuchlý, Jaroslav. "Bayes unbiased estimation in a model with three variance components." Applications of Mathematics 34, no. 5 (1989): 375–86. http://dx.doi.org/10.21136/am.1989.104365.

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Rozprawy doktorskie na temat "Variance model"

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Xiao, Yan. "Evaluating Variance of the Model Credibility Index." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/39.

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Model credibility index is defined to be a sample size under which the power of rejection equals 0.5. It applies goodness-of-fit testing thinking and uses a one-number summary statistic as an assessment tool in a false model world. The estimation of the model credibility index involves a bootstrap resampling technique. To assess the consistency of the estimator of model credibility index, we instead study the variance of the power achieved at a fixed sample size. An improved subsampling method is proposed to obtain an unbiased estimator of the variance of power. We present two examples to inte
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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.

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In experimental or quasi-experimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and non-repeated factors (termed group-by-occasion interaction here). If a condition derived by Thomas (1983), multivariate
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Moravec, Radek. "Oceňování opcí a variance gama proces." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-18707.

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The submitted work deals with option pricing. Mathematical approach is immediately followed by an economic interpretation. The main problem is to model the underlying uncertainities driving the stock price. Using two well-known valuation models, binomial model and Black-Scholes model, we explain basic principles, especially risk neutral pricing. Due to the empirical biases new models have been developped, based on pure jump process. Variance gamma process and its special symmetric case are presented.
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Abdumuminov, Shuhrat, and David Emanuel Esteky. "Black-Litterman Model: Practical Asset Allocation Model Beyond Traditional Mean-Variance." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-32427.

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This paper consolidates and compares the applicability and practicality of Black-Litterman model versus traditional Markowitz Mean-Variance model. Although well-known model such as Mean-Variance is academically sound and popular, it is rarely used among asset managers due to its deficiencies. To put the discussion into context we shed light on the improvement made by Fisher Black and Robert Litterman by putting the performance and practicality of both Black- Litterman and Markowitz Mean-Variance models into test. We will illustrate detailed mathematical derivations of how the models are constr
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Tjärnström, Fredrik. "Variance expressions and model reduction in system identification /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek730s.pdf.

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Finlay, Richard. "The Variance Gamma (VG) Model with Long Range Dependence." University of Sydney, 2009. http://hdl.handle.net/2123/5434.

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Doctor of Philosophy (PhD)<br>This thesis mainly builds on the Variance Gamma (VG) model for financial assets over time of Madan & Seneta (1990) and Madan, Carr & Chang (1998), although the model based on the t distribution championed in Heyde & Leonenko (2005) is also given attention. The primary contribution of the thesis is the development of VG models, and the extension of t models, which accommodate a dependence structure in asset price returns. In particular it has become increasingly clear that while returns (log price increments) of historical financial asset time series appear as a re
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Robinson, Timothy J. "Dual Model Robust Regression." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/11244.

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In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin (1987) proposes a parametric dual model in which a log linear dependence of the variances on a set of explanatory variables is assumed. Aitkin's parametric approach is an iterative one providing estimates for the parameters in the mean and variance models through joint maximum likelihood. Est
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Roh, Kyoungmin. "Evolutionary variance of gene network model via simulated annealing." [Ames, Iowa : Iowa State University], 2008.

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Letsoalo, Marothi Peter. "Assessing variance components of multilevel models pregnancy data." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2873.

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Thesis (M. Sc. (Statistics)<br>Most social and health science data are longitudinal and additionally multilevel in nature, which means that response data are grouped by attributes of some cluster. Ignoring the differences and similarities generated by these clusters results to misleading estimates, hence motivating for a need to assess variance components (VCs) using multilevel models (MLMs) or generalised linear mixed models (GLMMs). This study has explored and fitted teenage pregnancy census data that were gathered from 2011 to 2015 by the Africa Centre at Kwa-Zulu Natal, South Africa. The exp
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Brien, Christopher J. "Factorial linear model analysis." Title page, table of contents and summary only, 1992. http://thesis.library.adelaide.edu.au/public/adt-SUA20010530.175833.

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"February 1992" Bibliography: leaf 323-344. Electronic publication; Full text available in PDF format; abstract in HTML format. Develops a general strategy for factorial linear model analysis for experimental and observational studies, an iterative, four-stage, model comparison procedure. The approach is applicable to studies characterized as being structure-balanced, multitiered and based on Tjur structures unless the structure involves variation factors when it must be a regular Tjur structure. It covers a wide range of experiments including multiple-error, change-over, two-phase, superimpos
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Książki na temat "Variance model"

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Faraway, Julian J. Extending Linear Model With R. London: Chapman & Hall/CRC, 2004.

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Schlicht, Ekkehart. Variance estimation in a random coefficients model. Bonn, Germany: IZA, 2006.

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Chang-Jin, Kim. In search of a model that an ARCH-type model may be approximating: The Markov model of heteroskedasticity. [Toronto, Ont: York University, Dept. of Economics, 1990.

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Hastie, Trevor. Exploring the nature of covariate effects in the proportional hazards model. Toronto: University of Toronto, Dept. of statistics, 1988.

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Boylan, John E. The compound Poisson demand model and the quadratic variance law. Coventry: University of Warwick. Warwick Business School Research Bureau, 1994.

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Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Taylor & Francis, 2016.

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McEntegart, Karen. A comparison of mean-variance and mean-semivariance capital asset models : evidence from the Irish stock market. Dublin: University College Dublin, 1994.

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Park, Hun Y. A comparison of a random variance model and the Black-Scholes model of pricing long-term European options. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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Data analysis and approximate models: Model choice, location-scale, analysis of variance, nonparametic regression and image analysis. Boca Raton: CRC Press, 2014.

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Johansen, Søren. The asymptotic variance of the estimated roots in a cointegrated vector autoregressive model. Florence: European University Institute, Department of Economics, 2001.

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Części książek na temat "Variance model"

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Särndal, Carl-Erik, Bengt Swensson, and Jan Wretman. "Variance Estimation." In Model Assisted Survey Sampling, 418–46. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4378-6_11.

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Hangay, George, Susan V. Gruner, F. W. Howard, John L. Capinera, Eugene J. Gerberg, Susan E. Halbert, John B. Heppner, et al. "Mean-Variance Model." In Encyclopedia of Entomology, 2313. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1761.

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Zimmerman, Dale L. "Inference for Variance–Covariance Parameters." In Linear Model Theory, 451–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_16.

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Zimmerman, Dale L. "Inference for Variance–Covariance Parameters." In Linear Model Theory, 325–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8_16.

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Chung, Kai Lai, and Farid AitSahlia. "Mean-Variance Pricing Model." In Undergraduate Texts in Mathematics, 329–58. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21548-8_9.

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Jalili-Kharaajoo, Mahdi, and Farhad Besharati. "Fuzzy Variance Analysis Model." In Computer and Information Sciences - ISCIS 2003, 537–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_67.

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Qin, Zhongfeng. "Credibilistic Mean-Variance-Skewness Model." In Uncertainty and Operations Research, 29–52. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1810-7_2.

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Qin, Zhongfeng. "Uncertain Random Mean-Variance Model." In Uncertainty and Operations Research, 131–49. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1810-7_8.

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Board, John L. G., Charles M. S. Sutcliffe, and William T. Ziemba. "Portfolio Theory: Mean-Variance Model." In Encyclopedia of Operations Research and Management Science, 1142–48. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_775.

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Malley, James D. "Linearization of the Basic Model." In Optimal Unbiased Estimation of Variance Components, 15–28. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_3.

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Streszczenia konferencji na temat "Variance model"

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Pardavi-horvath, M., E. Della Terre, F. Vajda, and G. Verrtesy. "A Variable-variance Preisach Model." In 1993 Digests of International Magnetics Conference. IEEE, 1993. http://dx.doi.org/10.1109/intmag.1993.642266.

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Brinckman, Kevin, William Calhoon, Stephen Mattick, Jeremy Tomes, and Sanford Dash. "Scalar Variance Model Validation for High-Speed Variable Composition Flows." In 44th AIAA Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-715.

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Jiang, Wendy Xi, Barry L. Nelson, and L. Jeff Hong. "Estimating Sensitivity to Input Model Variance." In 2019 Winter Simulation Conference (WSC). IEEE, 2019. http://dx.doi.org/10.1109/wsc40007.2019.9004684.

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Wan, Shuping. "Mean-variance Portfolio Model with Consumption." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345085.

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Hoe, Lam Weng, and Lam Weng Siew. "Portfolio optimization with mean-variance model." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952526.

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Chen, Guohua, and Xiaolian Liao. "Credibility Mean-Variance-skewness Portfolio Selection Model." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5659059.

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Pan, Qiming, and Xiaoxia Huang. "Mean-Variance Model for International Portfolio Selection." In 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2008. http://dx.doi.org/10.1109/euc.2008.16.

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Mahdi Jalili Kharaajoo, Mahdi Jalili Kharaajoo, and Hassan Ebrahimirad Hassan Ebrahimirad. "A note on fuzzy variance analysis model." In 2003 International Symposium on Signals, Circuits and Systems. IEEE, 2003. http://dx.doi.org/10.1109/scs.2003.1226960.

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Bahnas, Mohamed, and Mohamed Al-Imam. "OPC model calibration considerations for data variance." In SPIE Advanced Lithography. SPIE, 2008. http://dx.doi.org/10.1117/12.776896.

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Boone-Sifuentes, Tanya, Antonio Robles-Kelly, and Asef Nazari. "Max-Variance Convolutional Neural Network Model Compression." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363347.

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Raporty organizacyjne na temat "Variance model"

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West, Kenneth. A Variance Bounds Test of the Linear Quardractic Inventory Model. Cambridge, MA: National Bureau of Economic Research, March 1985. http://dx.doi.org/10.3386/w1581.

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Gelfand, Alan E., and Dipak K. Dey. Improved Estimation of the Disturbance Variance in a Linear Regression Model. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada210272.

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Tong, C. Toward a more robust variance-based global sensitivity analysis of model outputs. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/923115.

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Rauscher, Harold M. The microcomputer scientific software series 3: general linear model--analysis of variance. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, 1985. http://dx.doi.org/10.2737/nc-gtr-86.

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Stock, James, and Mark Watson. Asymptotically Median Unbiased Estimation of Coefficient Variance in a Time Varying Parameter Model. Cambridge, MA: National Bureau of Economic Research, August 1996. http://dx.doi.org/10.3386/t0201.

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Hacker, Joshua P., Cari G. Kaufman, and James Hansen. State-Space Analysis of Model Error: A Probabilistic Parameter Estimation Framework with Spatial Analysis of Variance. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574466.

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Nelson, Charles, and Chang-Jin Kim. The Time-Varying-Parameter Model as an Alternative to ARCH for Modeling Changing Conditional Variance: The Case of Lucas Hypothesis. Cambridge, MA: National Bureau of Economic Research, September 1988. http://dx.doi.org/10.3386/t0070.

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Odom, Robert I. Seabed Variability and Its Influence on Acoustic Prediction Uncertainty Model and Data Variance and Resolution: How Do We Quantify Uncertainty? Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada628078.

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Odom, Robert I. Seabed Variability and its Influence on Acoustic Prediction Uncertainty. Model and Data Variance and Resolution: How Do We Quantify Uncertainty? Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada630037.

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Odom, Robert I. Seabed Variability and its Influence on Acoustic Prediction Uncertainty Model and Data Variance and Resolution: How Do We Quantify Uncertainty? Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada627080.

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