Three forms of multivariate analysis, one very classical and the other two relatively new and little-known, are showcased and enhanced: the first is the Singular Value Decomposition (SVD), which is at the heart of many statistical, and now geostatistical, techniques; the second is the method of Variogram Analysis, which is one way of investigating spatial correlation in one or several variables; and the third is the process of interpolation known as cokriging, a method for optimizing the estimation of multivariate data based on the information provided through variogram analysis. The SVD is described in detail, and it is shown that the SVD can be generalized from its familiar matrix (two-dimensional) case to three, and possibly n, dimensions. This generalization we call the "Tensor SVD" (or TSVD), and we demonstrate useful applications in the field of geostatistics (and indicate ways in which it will be useful in other areas). Applications of the SVD to the tools of geostatistics are described: in particular, applications dependent on the TSVD, including variogram modelling in coregionalization. Variogram analysis in general is explored, and we propose broader use of an old tool (which we call the "corhogram ", based on the variogram) which proves useful in helping one choose variables for multivariate interpolation. The reasoning behind kriging and cokriging is discussed, and a better algorithm for solving the cokriging equations is developed, which results in simultaneous kriging estimates for comparison with those obtained from cokriging. Links from kriging systems to kernel systems are made; discovering kerneIs equivalent to kriging systems will be useful in the case where data are plentiful. Finally, some results of the application of geostatistical techniques to a data set concerning nitrate pollution in the West Salt River Valley of Arizona are described.

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<p>The 24-item scale has been used extensively to assess the attitudes towards AIDS precautions. This study investigated the usefulness and validity of the instrument in a South African setting, fourteen years after the development of the instrument. If a new structure could be found statistically, the HIV/AIDS prevention strategies could be more effective in aiding campaigns to change attitudes and sexual behaviour.</p>

Approved for public release; distribution is unlimited / This study models DoD suicides as a Poisson process to detect departures from usual variation using a self-starting control chart scheme. Methods are implemented in a Microsoft Excel spreadsheet with Visual Basic macros for ease of use. Persistent shifts in the process mean are detected in the following months for each service component. Army: August 1985 (increase), September 1987 (decrease), April 1991 (increase), November 1997 (decrease), and September 2001 (decrease). Navy: December 1990 (decrease), January 1993 (increase), May 1994 (decrease), July 1995 (increase), and March 1996 (decrease). Marine Corps: January 1993 (increase) and March 1998 (decrease). Air Force: January 1988 (increase), April 1990 (decrease), November 1994 (increase), November 1998 (decrease), and April 1999 (decrease). / Commander, United States Navy

M.A. / The purpose of this study is to document and build an econometric model of the demand in the South African Clothing industry. It is important to study the clothing industry because it is labour intensive and thus its growth and development could contribute positively toward eradicating the unemployment problem in South Africa. With globalization of world economies and South Africa being a signatory to the GATT/WTO, the implications for this industry are manifold. The opening chapter lists the problem statement, identifies the method of research utilised and the relevance of the study. Chapter two looks at demand theory, particularly with regard to the quantitative techniques involved in its estimation. It focusses on regression theory and the evaluation of results generated. The third chapter gives a background to the South African clothing industry, and touches on amongst others aspects of current importance such as trade reform, international best practice and the key issues the industry has to deal with. Chapter four looks at the econometrics aspects of the study. A near perfect forecast was obtained, which attests to the stability and superiority of the model which is presented. The main findings of this study are that it is supply considerations such as the wage bill, costs of inputs (eg textile materials) etc which play an important part in the survival and prosperity of the industry. It is also reveals the fact that low productivity levels could be easily and quickly rectified through the introduction of new organizational practices and human resource development, development of quick response relationships and training to support new organizational practices. The study further and finally asserts that, while trade reform could necessitate painful adjustments the industry could actually come out a stronger world player

A key challenge for the insurance industry is to charge each customer an appropriate price for the risk they represent. Risk varies widely from customer to customer, and a deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. The goal of this project is to see how well various statistical methods perform in predicting bodily injury liability Insurance claim payments based on the characteristics of the insured customer’s vehicles for this particular dataset from Allstate Insurance Company.We tried several statistical methods, including logistic regression, Tweedie’s compound gamma-Poisson model, principal component analysis (PCA), response averaging, and regression and decision trees. From all the models we tried, PCA combined with a with a Regression Tree produced the best results. This is somewhat surprising given the widespread use of the Tweedie model for insurance claim prediction problems.

Thesis (D.Sc.)--University of the Witwatersrand, 1963

In this thesis, we attempt to address some of these questions. First, we have devised a scheme to extract information of the plumes from simultaneous velocity and temperature measurements. Our method makes explicit use of the physical intuition that the velocity of the buoyant structures, e.g. plumes, should be related to the temperature fluctuation, in some apriori unknown manner as they are generated by buoyancy. Our scheme involves a decomposition of the local velocity measurement into two parts. The part that is correlated with some function of the temperature fluctuation measured at the same time is taken as the velocity of the plumes. Applying this scheme to measurements taken at the center and near the sidewall of the convection cell where the dominant buoyant structures are plumes, we have found the temperature dependence of the plume velocity at these two locations and understood our results from the equations of motion. Using these results of the temperature dependence of the plume velocity, we (i) conclude that heat is not mainly transported through the central region of the convection cell and (ii) obtain a relation between the scaling behavior of the plume velocity structure functions and the temperature structure functions that is different from what is implied by Bolgiano-Obukhov scaling. Then we have studied the possible effects of the large-scale mean circulation on the velocity and temperature statistics using simplified shell models of turbulent convection. We have introduced a large-scale mean flow into two shell models and found that its presence does not change the scaling behavior of velocity and temperature. / In turbulent thermal convection, velocity and temperature measurements taken at a point display complex fluctuations in time. On the other hand, visualization of the flow reveals recurring coherent structures. One prominent flow structure is a plume, which is generated from the thermal boundary layers by buoyancy. Another flow structure is a large-scale mean circulation that spans the entire convection cell. At least two strategies can be employed to study turbulent thermal convection or turbulent flows in general. One is to analyze and understand the fluctuations of the local measurements. The other is to characterize the coherent structures and study and understand their dynamics. These two approaches are not independent but provide complementary knowledge of the flows. Interesting questions hence include whether and how information about the ordered flow structures can be extracted from the fluctuating local measurements and how the presence of the ordered flow structures might affect the statistics of the fluctuations. / Guo, Hao = 热对流湍流中的统计特性与结构 / 郭昊. / "January 2007." / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6036. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 62-66). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Title and abstract in English and Chinese. / School code: 1307. / Guo, Hao = Re dui liu tuan liu zhong de tong ji te xing yu jie gou / Guo Hao.

by Vincent Hung Hin Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 76-77). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter §1.1 --- Classical approaches and their limitations --- p.1 / Chapter §1.2 --- New approach --- p.3 / Chapter Chapter 2 --- Two-dimensional Ordinal Square Tables --- p.5 / Chapter §2.1 --- Model --- p.5 / Chapter §2.2 --- Maximum likelihood estimator --- p.7 / Chapter §2.3 --- Optimization procedure --- p.8 / Chapter §2.4 --- Useful hypotheses --- p.9 / Chapter §2.5 --- Simulation study --- p.11 / Chapter §2.6 --- A real example --- p.18 / Chapter §2.7 --- Comparison of new and classical approaches --- p.22 / Chapter Chapter 3 --- Multi-dimensional Ordinal Tables --- p.25 / Chapter §3.1 --- Partition maximum likelihood estimator --- p.26 / Chapter §3.2 --- Optimization procedure --- p.28 / Chapter §3.3 --- Useful hypotheses --- p.37 / Chapter §3.4 --- Simulation study --- p.39 / Chapter Chapter 4 --- Conclusion --- p.45 / Tables --- p.48 / Appendix --- p.74 / References --- p.77

Chan Wing-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 43-45). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation of Volatility --- p.5 / Chapter 2.1 --- A revisit to the RiskMetrics --- p.6 / Chapter 2.2 --- Predicting Multiple-period of Volatilities --- p.7 / Chapter 2.3 --- Performance Measures --- p.11 / Chapter 2.4 --- Nonparametric Estimation of Quantiles --- p.13 / Chapter 3 --- Univariate Prediction --- p.15 / Chapter 3.1 --- Piecewise Constant Technique --- p.16 / Chapter 3.2 --- Piecewise Linear Technique --- p.22 / Chapter 4 --- Bivariate Prediction --- p.27 / Chapter 4.1 --- Model Selection --- p.28 / Chapter 4.2 --- Piecewise Linear with Discontinuity --- p.29 / Chapter 4.3 --- Piecewise Linear Technique --- p.35 / Chapter 5 --- Conclusions --- p.41 / Bibliography --- p.43