No description available.

published_or_final_version / Business Administration / Master / Master of Business Administration

Raw product value of vegetables for processing in the Northwest used to be established by a competitive market involving proprietary processors and growers. Due to the relocation of proprietary processors to the Midwest, this competitive market has eroded forcing cooperative processors to seek other means to set raw product values. In the absence of a competitive market for raw product, cooperatives must rely on an average of last year's prices paid by processors in a given region to value raw product. This method of lagged averages may be resulting in misallocated contracted acreage to grower-members of cooperatives, and inappropriate production levels of the processed good given market conditions. Therefore, the principal objective of this research is to develop and evaluate alternative methods of forecasting raw product value. Since the market for processed vegetables at the retail level is competitive, one alternative method employed was to use a forecast of supply and determinants of demand affecting retail price to forecast raw product value. These explanatory variables were regressed against raw product values of various crops obtained from a northwest processing and marketing cooperative. The raw product values were expressed as net returns/acre to the crops under investigation. The estimated equations, which had adjusted R²'s ranging from .267 to .851, were used to forecast raw product value. A second forecasting method investigated in this study was an exponential smoothing model. Raw product value forecasts were generated over two different time horizons, identified by the cooperatives' accounting procedures. The two alternative forecasting methods were compared to each other, and to the method currently in use by the cooperative, with the aim of determining the most accurate forecasting technique. Results showed that both the econometric and smoothing approaches fit the data better over the estimation period than did a naive lagged price estimate resembling the present method in use by the cooperative. The econometric method also fit the data better than did the smoothing approach. The econometric model provided poor forecasts for the longer forecast horizon, but proved to be effective in the shorter. The smoothing technique forecasted more effectively in the longer forecast horizon as compared with the shorter. These results suggest the importance of the forecast horizon in determining the more appropriate forecasting technique. Both forecasting techniques proposed in this study produced forecasts which were more accurate than the cooperative's present method at least half of the time. This suggests that viable alternatives to the present method of establishing raw product value exist for agricultural cooperatives. / Graduation date: 1986

Mode of access: World Wide Web. / Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 410-414). / Electronic reproduction. / Also available by subscription via World Wide Web / xxxi, 414 leaves, bound ill. (some col.), col. maps 29 cm

This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).

Operational planning of water resource systems like reservoirs and hydropower plants calls for real-time forecasting of reservoir inflow. Reservoir daily inflow forecasts provide a warning of impending floods or drought conditions and help to optimize operating policies for reservoir management based on a fine time scale. The aim of this study was to determine the best model for daily reservoir inflow prediction through linear regression, exponential smoothing and artificial neural network (ANN) techniques. The Hedi reservoir, the third largest reservoir in south China with a 1.144 x 109 m 3, was selected as the study site. The performance of these forecasting models, in terms of forecasting accuracy, efficiency of model development and adaptability for future prediction, were compared to one another. All models performed well during the dry season (inflow with low variability), while the non-linear ANNs were superior to other models in frontal rainy season and typhoon season (inflow with high variability). The performance of ANN models were hardly affected by the high degree of uncertainty and variability inherent to the rainy season. Stepwise selection was very helpful in identifying significant variables for regression models and ANNs. This procedure reduced ANN's size and greatly improved forecasting accuracy for ANN models. The impact of training data series, model architecture and network internal parameters on ANNs performances were also addressed in this study. The overall evaluation indicates that ANNs are an effective and robust tool for input-output mapping under more extreme and variable conditions. ANNs provide an alternative forecasting approach to conventional time series forecasting models for daily reservoir inflow prediction.

No description available.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2017. / With an increase in opportunistically-collected data, statistical methods that can accommodate unstructured designs are increasingly useful. Spatial capturerecapture (SCR) has such potential, but its applicability for species that are strongly gregarious is uncertain. It assumes that average animal locations are spatially random and independent, which is violated for gregarious species. I used a data set for African elephants (Loxodonta africana) and data simulation to assess bias and precision of SCR population density estimates given violations in location independence. I found that estimates were negatively biased and likely too precise if non-independence was ignored. Encounter heterogeneity models produced more realistic precision but density estimates were positively biased. Lowest bias was achieved by estimating density of groups, group size, and then multiplying to estimate overall population density. Such findings have important implications for the reliability of population density estimates where data are collected by unstructured means. / LG2017

Pang Suet-Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 85-87). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.1.1 --- Survival Analysis --- p.2 / Chapter 1.1.2 --- Tree、-structured Statistical Method --- p.2 / Chapter 1.1.3 --- Mortality Study --- p.3 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.3 --- Background Information --- p.4 / Chapter 1.4 --- Data Content --- p.7 / Chapter 1.5 --- Thesis Outline --- p.8 / Chapter 2 --- Imputation and File Splitting --- p.10 / Chapter 2.1 --- Imputation of Missing Values --- p.10 / Chapter 2.1.1 --- Purpose of Imputation --- p.10 / Chapter 2.1.2 --- Procedure of Hot Deck Imputation --- p.11 / Chapter 2.1.3 --- List of Variables for Imputation --- p.12 / Chapter 2.2 --- File Splitting --- p.14 / Chapter 2.2.1 --- Splitting by Gender --- p.14 / Chapter 2.3 --- Splitting for Validation Check --- p.1G / Chapter 3 --- Cox Hazard Model --- p.17 / Chapter 3.1 --- Basic Idea --- p.17 / Chapter 3.1.1 --- Survival Analysis --- p.17 / Chapter 3.1.2 --- Survivor Function --- p.18 / Chapter 3.1.3 --- Hazard Function --- p.18 / Chapter 3.2 --- The Cox Proportional Hazards Model --- p.19 / Chapter 3.2.1 --- Kaplan-Meier Estimate and Log-Rank Test --- p.20 / Chapter 3.2.2 --- Hazard Ratio --- p.23 / Chapter 3.2.3 --- Partial Likelihood --- p.24 / Chapter 3.3 --- Extension of the Cox Proportional Hazards Model for Time-dependent Variables --- p.25 / Chapter 3.3.1 --- Modification of the Cox's Model --- p.25 / Chapter 3.4 --- Results of Model Fitting --- p.26 / Chapter 3.4.1 --- Extract the Significant Covariates from the Models --- p.31 / Chapter 3.5 --- Model Interpretation --- p.32 / Chapter 4 --- CART --- p.37 / Chapter 4.1 --- CART Procedure --- p.38 / Chapter 4.2 --- Selection of the Splits --- p.39 / Chapter 4.2.1 --- Goodness of Split --- p.39 / Chapter 4.2.2 --- Type of Variables --- p.40 / Chapter 4.2.3 --- Estimation --- p.40 / Chapter 4.3 --- Pruning the Tree --- p.41 / Chapter 4.3.1 --- Misclassification Cost --- p.42 / Chapter 4.3.2 --- Class Assignment Rule --- p.44 / Chapter 4.3.3 --- Minimal Cost Complexity Pruning --- p.44 / Chapter 4.4 --- Cross Validation --- p.47 / Chapter 4.4.1 --- V-fold Cross-validation --- p.47 / Chapter 4.4.2 --- Selecting the right sized tree --- p.49 / Chapter 4.5 --- Missing Value --- p.49 / Chapter 4.6 --- Results of CART program --- p.51 / Chapter 4.7 --- Model Interpretation --- p.53 / Chapter 5 --- Model Prediction --- p.58 / Chapter 5.1 --- Application to Test Sample --- p.58 / Chapter 5.1.1 --- Fitting test sample to Cox's Model --- p.59 / Chapter 5.1.2 --- Fitting test sample to CART model --- p.61 / Chapter 5.2 --- Comparison of Model Prediction --- p.62 / Chapter 5.2.1 --- Misclassification Rate --- p.62 / Chapter 5.2.2 --- Misclassification Rate of Cox's model --- p.63 / Chapter 5.2.3 --- Misclassification Rate of CART model --- p.64 / Chapter 5.2.4 --- Prediction Result --- p.64 / Chapter 6 --- Conclusion --- p.67 / Chapter 6.1 --- Comparison of Results --- p.67 / Chapter 6.2 --- Comparison of the Two Statistical Techniques --- p.68 / Chapter 6.3 --- Limitation --- p.70 / Appendix A: Coding Description for the Health Factors --- p.72 / Appendix B: Log-rank Test --- p.75 / Appendix C: Longitudinal Plot of Time Dependent Variables --- p.76 / Appendix D: Hypothesis Testing of Suspected Covariates --- p.78 / Appendix E: Terminal node report for both gender --- p.81 / Appendix F: Calculation of Critical Values --- p.83 / Appendix G: Distribution of Missing Value in Learning sample and Test Sample --- p.84 / Bibliography --- p.85