by Lawrence Chi-Ho Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-97). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter Chapter 2 --- Holt-Winters Forecasting Procedure --- p.6 / Chapter 2.1 --- Exponential Smoothing and Holt-Winters Method --- p.6 / Chapter 2.2 --- Relationships Between Holt-Winters models and ARIMA Models --- p.13 / Chapter 2.2.1 --- A Steady Model --- p.14 / Chapter 2.2.2 --- A Growth Model --- p.15 / Chapter 2.2.3 --- The Three-Parameter Holt-Winters Model --- p.18 / Chapter 2.3 --- Some Practical Issues --- p.19 / Chapter 2.3.1 --- Normalizing the Seasonal Factors --- p.20 / Chapter 2.3.2 --- Choosing Starting Values --- p.20 / Chapter 2.3.3 --- Choosing the Smoothing Parameters --- p.22 / Chapter Chapter 3 --- Methods of Constructing Simultaneous Prediction Intervals --- p.24 / Chapter 3.1 --- Three Approximation Procedures --- p.25 / Chapter 3.1.1 --- Bonferroni-type Inequality --- p.26 / Chapter 3.1.2 --- Product-type Inequality --- p.28 / Chapter 3.1.3 --- Chi-square-type Inequality --- p.30 / Chapter 3.2 --- The 'Exact' Procedure --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter Chapter 4 --- An Illustrative Example --- p.33 / Table 4.1 - 4.7 --- p.47 / Figure 4.1 - 4.5 --- p.55 / Chapter Chapter 5 --- Simulation Study --- p.60 / Chapter 5.1 --- Holt-Winters Forecasting Procedure for Optimal Model --- p.60 / Chapter 5.2 --- Holt-Winters Forecasting Procedure for Some Non-optimal Models --- p.66 / Chapter 5.3 --- A Comparison of Box-Jenkins Method and Holt-Winters Forecasting Procedure --- p.68 / Chapter 5.4 --- Conclusion --- p.74 / Table 5.1-5.10 --- p.75 / Chapter Chapter 6 --- Further Research --- p.82 / APPENDIXES --- p.87 / REFERENCES --- p.91

In this thesis, we have considered the inference aspects of sampling from a finite population. There are significant differences between traditional statistical inference and finite population sampling inference. In the case of finite population sampling, the statistician is free to choose his own sampling design and is not confined to independent and identically distributed observations as is often the case with traditional statistical inference. We look at the correspondence between the sampling design and the sampling scheme. We also look at methods used for drawing samples. The non – existence theorems (Godambe (1955), Hanurav and Basu (1971)) are also discussed. Since the minimum variance unbiased estimator does not exist for infinite populations, a number of estimators need to be considered for estimating the same parameter. We discuss the admissible properties of estimators and the use of sufficient statistics and the Rao-Blackwell Theorem for the improvement of inefficient inadmissible estimators. Sampling strategies using auxiliary information, relating to the population, need to be used as no sampling strategy can provide an efficient estimator of the population parameter in all situations. Finally few well known sampling strategies are studied and compared under a super population model. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2007.

Thesis (Ph. D.)--University of California, San Diego, 1999. / Vita. Includes bibliographical references.

Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, June 2007. / Thesis Advisor(s): Christian Darken. "June 2007." Includes bibliographical references (p. 47). Also available in print.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 127-132).

M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

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A solution to the regression model y = xβ+ε is usually obtained using ordinary least squares. However, when the condition of multicollinearity exists among the regressor variables, then many qualities of this solution deteriorate. The qualities include the variances, the length, the stability, and the prediction capabilities of the solution. An analysis called ridge regression introduced a solution to combat this deterioration (Hoerl and Kennard, 1970a). The method uses a solution biased by a parameter k. Many methods have been developed to determine an optimal value of k. This study chose to investigate three little used methods of determining k: the PRESS statistic, Mallows' C_k. statistic, and DF-trace. The study compared the prediction capabilities of the three methods using data that contained various levels of both collinearity and leverage. This was completed by using a Monte Carlo experiment. / Master of Science

The purpose of this research is to study and improve decision accuracy in the real world. Spot price prediction of petroleum products, in a budgeting context, is the task chosen to study prediction accuracy. Prediction accuracy of executives in a multinational oil company is examined. The Brunswik Lens Model framework is used to evaluate prediction accuracy. Predictions of the individuals, the composite group (mathematical average of the individuals), the interacting group, and the environmental model were compared. Predictions of the individuals were obtained through a laboratory experiment in which experts were used as subjects. The subjects were required to make spot price predictions for two petroleum products. Eight predictor variables that were actually used by the subjects in real-world predictions were elicited through an interview process. Data for a 15 month period were used to construct 31 cases for each of the two products. Prediction accuracy was evaluated by comparing predictions with the actual spot prices. Predictions of the composite group were obtained by averaging the predictions of the individuals. Interacting group predictions were obtained ex post from the company's records. The study found the interacting group to be the least accurate. The implication of this finding is that even though an interacting group may be desirable for information synthesis, evaluation, or working toward group consensus, it is undesirable if prediction accuracy is critical. The accuracy of the environmental model was found to be the highest. This suggests that apart from random error, misweighting of cues by individuals and groups affects prediction accuracy. Another implication of this study is that the environmental model can also be used as an additional input in the prediction process to improve accuracy.

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Ph.D.