Książki na temat „Variance model”

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

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

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.

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

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

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

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

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.

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

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

Scott, Louis O. Random variance option pricing: Empirical tests of the model and delta-sigma hedging. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1988.

Chabi-Yo, Fousseni. Conditioning information and variance bounds on pricing kernels with higher-order moments: Theory and evidence. Ottawa: Bank of Canada, 2006.

Oomen, Roel C. A. Using high frequency stock market index data to calculate, model & forecast realized return variance. San Domenico: European University Institute, Department of Economics, 2001.

Chang-Jin, Kim. Sources of monetary growth uncertainty and economic activity: The time-varying-parameter model with heteroskedasticity in the disturbance terms. [Toronto, Ont: York University, Dept. of Economics, 1990.

Amiri-Simkooei, AliReza. Variance Component Estimation In Linear Models: Theoretical And Practical Aspects On Global Positioning System. Saarbrücken , Germany: VDM Verlag Dr. Müller, 2010.

Ma, Xiaofang. Computation of the probability density function and the cumulative distribution function of the generalized gamma variance model. Ottawa: National Library of Canada, 2002.

Almimi, Ashraf. Split-Plot Designs: Follow-Up Experiments, Missing Observations, and Model Adequacy Checking. Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2010.

Harvey, Andrew. Multivariate stochastic variance models. London: London School ofEconomics Financial Markets Group, 1992.

Harvey, Andrew. Multivariate stochastic variance models. London: LSE Financial Markets Group, 1992.

Variance components estimation: Mixed models, methodologies and applications. London: Chapman & Hall, 1997.

Verma, J. P. Repeated Measures Design For Empirical Researchers. Chichester, West Sussex, UK: Wiley-Blackwell, 2015.

Linear models with R. Boca Raton, Fla: Chapman & Hall/CRC, 2005.

Linear models with R. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

Sahai, Hardeo, i Mario Miguel Ojeda. Analysis of Variance for Random Models. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8168-5.

Srivastava, M. S. Generalized multivariate analysis of variance models. Toronto: University of Toronto, Dept. of Statistics, 1997.

Longman, Richard W. Variance and bias confidence criteria for ERA modal parameter identification. [New York]: American Institute of Aeronautics and Astronautics, 1988.

1959-, Ageel Mohammed I., red. The analysis of variance: Fixed, random, and mixed models. Boston: Birkhäuser, 2000.

Harvey, A. C. Estimation and testing of stochastic variance models. London: Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1993.

Searle, S. R. Linear models for unbalanced data. New York: Wiley, 1987.

Heinen, Ton. Discrete latent variable models. Tilburg, Netherlands: Tilburg University Press, 1993.

Lanitis, Andreas. Model-based recognition of variable objects. Manchester: University of Manchester, 1995.

Sharma, H. L. Experimental Designs And Survey Sampling: Methods And Applications. Udaipur, Rajasthan, India: Agrotech Publishing Academy, 2010.

Ruiz, Esther. Quasi-maximum likelihood estimation of stochastic variance models. London: London School of Economics and Political Science, Suntory Toyota International Centre for Economics and Related Disciplines, 1992.

Dunson, David B., red. Random Effect and Latent Variable Model Selection. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5.

Schweitzer, Marcell. Break-even analyses: Basic model, variants, extensions. Chichester [Engladn]: Wiley, 1992.

Sahai, Hardeo. The Analysis of Variance: Fixed, Random and Mixed Models. Boston, MA: Birkhäuser Boston, 2000.

Plane answers to complex questions: The theory of linear models. Wyd. 2. New York: Springer, 1996.

Plane answers to complex questions: The theory of linear models. Wyd. 3. New York: Springer, 2002.

Plane answers to complex questions: The theory of linear models. Wyd. 4. New York: Springer, 2011.

Christensen, Ronald. Plane answers to complex questions: The theory of linear models. New York: Springer-Verlag, 1987.

Ronald, Christensen. Plane answers to complex questions: The theory of linear models. Wyd. 3. New York: Springer, 2010.

Ronald, Christensen. Plane answers to complex questions: The theory of linear models. New York: Springer-Verlag, 1987.

Clarke, Brenton R. Linear models: The theory and application of analysis of variance. Hoboken, N.J: Wiley, 2008.

Ronald, Christensen. Plane answers to complex questions: The theory of linear models. Wyd. 2. New York: Springer, 1996.

Poduri S.R.S. Rao. Variance Components Estimation: Mixed Models, Methodologies and Applications (Monographs on Statistics & Applied Probability). London, UK: Chapman & Hall/CRC, 1997.

Hocking, R. R. Methods and Applications of Linear Models. New York: John Wiley & Sons, Ltd., 2005.

Bartholomew, David, Martin Knott i Irini Moustaki. Latent Variable Models and Factor Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119970583.

Bartholomew, David J. Latent variable models and factor analysis. Wyd. 2. London: Arnold, 1999.

Bartholomew, David J. Latent variable models and factor analysis. London: C. Griffin, 1987.

Burns, Tom, i Mike Firn. Model variance and model fidelity: The lessons from ACT. Redaktorzy Tom Burns i Mike Firn. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198754237.003.0004.