| CODE | SOR3222 | ||||||||
| TITLE | Nonlinear and Nonparametric Regression Analysis | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 6 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||
| DESCRIPTION | - Parameter estimation in non-linear regression models using non-linear least squares, generalised least squares, maximum likelihood and concentrated likelihood estimation methods; - Univariate kernel density estimation and asymptotic properties; - Bandwidth selectors for univariate kernel density estimators; - Multivariate kernel density estimation and asymptotic properties; - Density derivative estimation; - Bandwidth selectors for multivariate kernel density estimators; - Kernel regression estimation and asymptotic properties; - Bandwidth selection using Plug-in rules and Cross-validation; - Kernel regression with mixed multivariate data; - Prediction and confidence intervals; - Local likelihood; - Non-parametric goodness of fit tests. Study-Unit Aims: - Familiarize students with different estimation methods used in nonlinear regression models, including maximum likelihood estimation, quasi-likelihood estimation, robust and Bayesian estimation; - Compare between different competing nonlinear regression and identify the best model fit; - Distinguish between different kernel smoothing methods. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Identify the limitations of linear regression models when the normality, linearity and homoscedasticity assumptions are violated; - Distinguish between different models and statistical techniques when analyzing different data sets, while recognizing the strengths and limitations of these models. 2. Skills: By the end of the study-unit the student will be able to: - Fit nonlinear and nonparametric regression models using the facilities of STATA; - Recognize variance heterogeneity, identifiability, ill-conditioning and parameter redundancy; - Use these models for prediction purposes. Main Text/s and any supplementary readings: - Seber G. A. F., Wild C.J. (2003) Nonlinear Regression, John Willy-Interscience Inc. - Garson G. D. (2012) Curve Fitting and Nonlinear Regression, Blue Book Series. - Hardle W. (1990) Applied Nonparametric Regression, Cambridge University Press. - Eubank R.L. (1988) Nonparametric Regression and Spline Smoothing, Marcel Dekker Inc. - Takezawa K. (2005) Introduction to Nonparametric Regression, John Wiley-Interscience Inc. - Green P.J., Silverman B.W. (1993) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach Chapman & Hall/CRC. - Gyorfi L., Kohler M., Krzyzak A., Walk H. (2002) Distribution-free theory of Nonparametric Regression, Springer. - Silverman B.W. (1986) Density estimation for Statistics and Data Analysis, Springer-Science and Business Media. - Simonoff J. S. (1998) Smoothing methods in Statistics, Springer |
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| ADDITIONAL NOTES | Pre-Requisite Study-Unit: SOR3221 Co-Requisite Study-Unit: SOR3211 |
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| STUDY-UNIT TYPE | Lecture | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Liberato Camilleri |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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