Efficient Portfolio Selection

Merak believed that an efficient frontier analysis method that combined the robustness of the Monte Carlo approach with the confidence of the Markowitz approach would be a very powerful tool for any industry. However, it soon became clear that there are other ways to address the problem that do not require a Monte Carlo component. Three subgroups were formed, and each developed a different approach for solving the problem. These were the Portfolio Selection Algorithm Approach, the Statistical Inference Approach, and the Integer Programming Approach

Based on results obtained at the Seventh Annual PIMS Industrial Problem Solving Workshop

Introduction The behaviour of consumers is believed to be influenced by many factors. Some of these factors include the individuals culture, social status, lifestyle and attitudes. Understanding how these complicated and interrelated factors drive the consumer is the primary goal of Manifold Data Mining. The question posed to the group was to 1) find an algorithm that predicts the likelihood of consumers to respond favourably to a given product. In addition, once this prediction is made for a given consumer the group was also asked to 2) develop a second algorithm that infers other statistical information regarding the consumer. Manifold Data Mining has developed innovative demographic and household spending pattern databases for six-digit postal codes in Canada. Their collection of information consists of both demographic and expenditure variables which are expressed through thousands of individually tracked factors. This large collection of information about consumer behaviour is typically referred to as a mine. Although very large in practice, for the purposes of this report, the data mine consisted of m individuals and n factors where m 2000 and n 50. Ideally, the first algorithm would identify a few factors in the data mine which would differentiate customers in terms of a particular product preference. Then the second algorithm would build on this information by looking for patterns in the data mine which would identify related areas of consumer spending. To test the algorithms two case studies were undertaken. The first study involved differentiating BMW and Honda car owners. The algorithms developed were reasonably successful at both finding questions that differentiate these two populations and identifying common characteristics amongst th

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Advances in life testing: Progressive censoring and generalized distributions

This thesis is presented in three sections: Section One (Progressive Type-II Censoring). Section Two (Generalized Distributions) and Section Three (Conclusions). In Section One of the thesis, a method of censoring known as progressive Type-II censoring is presented. Mathematical properties of the progressive Type-II censored order statistics arising from this type of censoring are established for particular as well as arbitrary distributions. Applications to inference, including best linear unbiased estimation and maximum likelihood estimation, are discussed, as well as simulation, and the question of optimal censoring patterns is also addressed through an extensive computational study. Section Two of the thesis concerns generalized distributions. Here, we introduce a shape parameter to the logistic and half logistic distributions and discuss the properties of the resulting distributions. Many recurrence relations for single and product moments of order statistics from these distributions are established. Best linear unbiased, maximum likelihood and moment estimation of parameters arising from these distributions are considered, and truncated versions of the distributions are also examined. Finally, we conclude with a number of questions and open problems which have yet to be addressed in the future.Doctor of Philosophy (PhD