A statistical approach to some mine valuation and allied problems on the Witwatersrand

Thesis (M.Sc.(Engineering))--University of the Witwatersrand, Faculty of Engineering, 1951

Universal Prediction Distribution for Surrogate Models

International audienceThe use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions. The main advantage of probabilistic approach is that it provides a measure of uncertainty associated with the surrogate model in the whole space. This uncertainty is an efficient tool to construct strategies for various problems such as prediction enhancement, optimization or inversion.In this paper, we propose a universal method to define a measure of uncertainty suitable for any surrogate model either deterministic or probabilistic. It relies on Cross-Validation (CV) sub-models predictions. This empirical distribution may be computed in much more general frames than the Gaussian one. So that it is called the Universal Prediction distribution (UP distribution).It allows the definition of many sampling criteria. We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm. We also discuss the use of the UP distribution for inversion problems. The performances of these new algorithms are studied both on toys models and on an engineering design problem

The price-to-book effect on the JSE: Valuation disparities and subsequent performance

The purpose of this study was to determine whether the relative out- or underperformance of a value portfolio versus a growth portfolio can be anticipated in advance by comparing a valuation difference multiple with the subsequent fiveyear relative performance of the value and growth portfolios. The valuation difference multiple was calculated as the median price-to-book value (P/B) ratio of the growth portfolio divided by the median P/B ratio of the value portfolio. Using monthly data for the period 1991 to 2011, this study found that in most instances the higher the valuation difference multiple, the higher the outperformance of the value portfolio over the subsequent five-year period, as compared to the growth portfolio

The price-to-book effect on the JSE : valuation disparities and subsequent performance

CITATION: Du Toit, S. G. & Krige, J. D. 2014. The price-to-book effect on the JSE : valuation disparities and subsequent performance. South African Journal of Business Management, 45(4): a143, doi:10.4102/sajbm.v45i4.143.The original publication is available at https://sajbm.orgThe purpose of this study was to determine whether the relative out- or underperformance of a value portfolio versus a growth portfolio can be anticipated in advance by comparing a valuation difference multiple with the subsequent fiveyear relative performance of the value and growth portfolios. The valuation difference multiple was calculated as the median price-to-book value (P/B) ratio of the growth portfolio divided by the median P/B ratio of the value portfolio. Using monthly data for the period 1991 to 2011, this study found that in most instances the higher the valuation difference multiple, the higher the outperformance of the value portfolio over the subsequent five-year period, as compared to the growth portfolio.https://sajbm.org/index.php/sajbm/article/view/143Publisher's versio

Constraint Handling in Efficient Global Optimization

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes. In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms. We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example.This research was partly funded by Tekes, the Finnish Funding Agency for Innovation (the DeCoMo project), and by the Engineering and Physical Sciences Research Council [grant numbers EP/N017195/1, EP/N017846/1]

The synergistic integration of computational fluid dynamics and experimental fluid dynamics for ground effect aerodynamics studies

This article highlights the ‘synergistic’ use of experimental fluid dynamics (EFD) and computational fluid dynamics (CFD), where the two sets of simulations are performed concurrently and by the same researcher. In particular, examples from the area of ground effect aerodynamics are discussed, where the major facility used was also designed through a combination of CFD and EFD. Three examples are than outlined, to demonstrate the insight that can be obtained from the integration of CFD and EFD studies. The case studies are the study of dimple flow (to enhance aerodynamic performance), the analysis of a Formula-style front wing and wheel, and the study of compressible flow ground effect aerodynamics. In many instances, CFD has been used to not only provide complementary information to an experimental study, but to design the experiments. Laser-based, non-intrusive experimental techniques were used to provide an excellent complement to CFD. The large datasets found from both experimental and numerical simulations have required a new methodology to correlate the information; a new post-processing method has been developed, making use of the kriging and co-kriging estimators, to develop correlations between the often disparate data types

Master Equation Study of Hydrogen Relaxation Using Complete Sets of State-to-state Transition Rates

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97096/1/AIAA2012-362.pd

On the Use of Upper Trust Bounds in Constrained Bayesian Optimization Infill Criterion

In order to handle constrained optimization problems with a large number of design variables, a new approach has been proposed to address constraints in a surrogate-based optimization framework. This approach focuses on sequential enrichment using adaptive surrogate models based on Bayesian optimization approach, and Gaussian process models. A constraints criterion using the uncertainty estimation of the Gaussian process models is introduced. Different evolutions of the algorithm, based on the accuracy of the constraints surrogate models, are used for selecting the infill sample points. The resulting algorithm has been tested on the well known modified Branin optimization problem

Effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. The VANISH Randomized Clinical Trial

IMPORTANCE: Norepinephrine is currently recommended as the first-line vasopressor in septic shock; however, early vasopressin use has been proposed as an alternative. OBJECTIVE: To compare the effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. DESIGN, SETTING, AND PARTICIPANTS: A factorial (2×2), double-blind, randomized clinical trial conducted in 18 general adult intensive care units in the United Kingdom between February 2013 and May 2015, enrolling adult patients who had septic shock requiring vasopressors despite fluid resuscitation within a maximum of 6 hours after the onset of shock. INTERVENTIONS: Patients were randomly allocated to vasopressin (titrated up to 0.06 U/min) and hydrocortisone (n = 101), vasopressin and placebo (n = 104), norepinephrine and hydrocortisone (n = 101), or norepinephrine and placebo (n = 103). MAIN OUTCOMES AND MEASURES: The primary outcome was kidney failure-free days during the 28-day period after randomization, measured as (1) the proportion of patients who never developed kidney failure and (2) median number of days alive and free of kidney failure for patients who did not survive, who experienced kidney failure, or both. Rates of renal replacement therapy, mortality, and serious adverse events were secondary outcomes. RESULTS: A total of 409 patients (median age, 66 years; men, 58.2%) were included in the study, with a median time to study drug administration of 3.5 hours after diagnosis of shock. The number of survivors who never developed kidney failure was 94 of 165 patients (57.0%) in the vasopressin group and 93 of 157 patients (59.2%) in the norepinephrine group (difference, -2.3% [95% CI, -13.0% to 8.5%]). The median number of kidney failure-free days for patients who did not survive, who experienced kidney failure, or both was 9 days (interquartile range [IQR], 1 to -24) in the vasopressin group and 13 days (IQR, 1 to -25) in the norepinephrine group (difference, -4 days [95% CI, -11 to 5]). There was less use of renal replacement therapy in the vasopressin group than in the norepinephrine group (25.4% for vasopressin vs 35.3% for norepinephrine; difference, -9.9% [95% CI, -19.3% to -0.6%]). There was no significant difference in mortality rates between groups. In total, 22 of 205 patients (10.7%) had a serious adverse event in the vasopressin group vs 17 of 204 patients (8.3%) in the norepinephrine group (difference, 2.5% [95% CI, -3.3% to 8.2%]). CONCLUSIONS AND RELEVANCE: Among adults with septic shock, the early use of vasopressin compared with norepinephrine did not improve the number of kidney failure-free days. Although these findings do not support the use of vasopressin to replace norepinephrine as initial treatment in this situation, the confidence interval included a potential clinically important benefit for vasopressin, and larger trials may be warranted to assess this further. TRIAL REGISTRATION: clinicaltrials.gov Identifier: ISRCTN 20769191