Dissertation (PhD)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1982. / Typescript. Vita. Description based on print version record. Includes bibliographical references (leaves 332-336).

Thesis (Ph. D.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.

A Research Report submitted to the Faculty of Science in partial fulfilment of the requirements for the degree of Master of Science in the School of Statistics and Actuarial Science. 26 May 2016 / Exponential smoothing is a recursive time series technique whereby forecasts are updated for each new incoming data values. The technique has been widely used in forecasting, particularly in business and inventory modelling. Up until the early 2000s, exponential smoothing methods were often criticized by statisticians for lacking an objective statistical basis for model selection and modelling errors. Despite this, exponential smoothing methods appealed to forecasters due to their forecasting performance and relative ease of use. In this research report, we apply three commonly used exponential smoothing methods to two datasets which exhibit both trend and seasonality. We apply the method directly on the data without de-seasonalizing the data first. We also apply a seasonal naive method for benchmarking the performance of exponential smoothing methods. We compare both in-sample and out-of-sample forecasting performance of the methods. The performance of the methods is assessed using forecast accuracy measures. Results show that the Holt-Winters exponential smoothing method with additive seasonality performed best for forecasting monthly rainfall data. The simple exponential smoothing method outperformed the Holt’s and Holt-Winters methods for forecasting daily temperature data.

Wong, Ka Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 46-49). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.4 / Chapter 2.1 --- Notation and Definitions --- p.4 / Chapter 2.2 --- Influence Function of β --- p.5 / Chapter 3 --- Methodology --- p.7 / Chapter 3.1 --- Robust Estimating Equations --- p.7 / Chapter 3.2 --- A General Algorithm for Robust GAM Estimation --- p.9 / Chapter 4 --- Asymptotic Equivalence --- p.12 / Chapter 5 --- Smoothing Parameter Selection --- p.16 / Chapter 5.1 --- Robust Cross-Validation --- p.17 / Chapter 5.2 --- Robust Information Criteria --- p.17 / Chapter 6 --- Multiple Covariates --- p.19 / Chapter 7 --- Simulation Study --- p.21 / Chapter 8 --- Real Data Examples --- p.26 / Chapter 8.1 --- Air Pollution Data --- p.26 / Chapter 8.2 --- Bronchitis Data --- p.28 / Chapter 9 --- Concluding Remarks --- p.31 / Chapter A --- Auxiliary Lemmas and Proofs --- p.32 / Chapter B --- Fisher Consistency Correction --- p.42 / Chapter B.1 --- Poisson distribution --- p.42 / Chapter B.2 --- Bernoulli distribution --- p.43 / Chapter C --- Derivation of (5.2) --- p.44 / Bibliography --- p.46

published_or_final_version / Statistics / Master / Master of Philosophy

Thesis (M.S.)--Ohio University, November, 1995. / Title from PDF t.p.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 79-86).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1983. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 242-248).

Thesis (Ph. D.)--University of Iowa, 2007. / Supervisors: George Woodworth, Matthew Bognar. Includes bibliographical references (leaves 72-73).