THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Grey Wolf Optimizer

Data Mining Practical Machine Learning Tools and Techniques

The Whale Optimization Algorithm

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

This article presents a brief description of nonlife claims reserving methods. Nonlife claims reserves are financial resources, set aside from all other insurance operations, as a provision for claims not completely settled at a specific time. Since these provisions constitute the largest liabilities carried by nonlife insurers, their determination affects many aspects of the business, such as ratemaking and the assessment of the insurer's net worth. From the actuarial point of view, reserving is a problem of estimation, or more precisely, forecasting. As a consequence, reserving methods range from very simple deterministic techniques to some very sophisticated ones, based on stochastic models for claim settlement. We present, in a structured way, the main approaches in the field, and provide the interested reader with references for further study.


Keywords:

nonlife insurance;
reserves;
IBNR ;
loss ratios;
chain ladder;
runoff triangle;
regression;
GLM ;
Bayesian methods;
MCMC

Nonlife Loss Reserving

Discrete-variable gate-model quantum machines are promising quantum systems considering their wide applicability. Being in their noisy-intermediate-scale era, they allow executing only circuits of limited complexity and fitting the machines' features. Thus, such systems implement a key and unavoidable tailoring process to produce the most possible compact and device-compliant circuit. The qubits' initialisation is a primary and complex step that can ease/jeopardise the tailoring process and restrict/extend the machine's computational capacities. Ultimately, this bottleneck can be responsible of making quantum leaps like quantum supremacy. As a step towards the former, this work investigates how evolutionary algorithms can enhance the qubits' initialisation by tackling it as a single-objective problem using a genetic algorithm. The experiments used instances representing 19 real IBM quantum machines with 7 to 65 qubits and 9 different qubit topologies. Also, 76 GHZ circuits of sizes 7-65 qubits and 25%-100% of entanglement were created and studied. Extensive standard and statistical comparisons have been made against the IBM qubit initialiser that is currently used in real quantum machines. Results showed that the proposal outperforms IBM in 64 instances and is similar to it in 10 ones, with an average circuit-compression gain up to 46%.

Genetic algorithm for qubits initialisation in noisy intermediate-scale quantum machines: the IBM case study

Chapter Preview . In this chapter we approach many of the topics of the previous chapters, but from a Bayesian viewpoint. Initially we cover the foundations of Bayesian inference. We then describe the Bayesian linear and generalized regression models. We concentrate on the regression models with zero-one and count response and illustrate the models with real datasets. We also cover hierarchical prior specifications in the context of mixed models. We finish with a description of a semi-parametric linear regression model with a nonparametric specification of the error term. We also illustrate its advantage with respect to the fully parametric setting using a real dataset. Introduction The use of Bayesian concepts and techniques in actuarial science dates back to Whitney (1918) who laid the foundations for what is now called empirical Bayes credibility. He noted that the solution of the problem “depends upon the use of inverse probabilities.” This is the term used by T. Bayes in his original paper (e.g., Bellhouse 2004). However, Ove Lundberg was apparently the first one to realize the importance of Bayesian procedures (Lundberg 1940). In addition, Bailey (1950) put forth a clear and strong argument in favor of using Bayesian methods in actuarial science. To date, the Bayesian methodology has been used in various areas within actuarial science; see, for example, Klugman (1992), Makov (2001), Makov, Smith, and Liu (1996), and Scollnik (2001).

Predictive Modeling Applications In Actuarial Science: Bayesian Regression Models

Universidad de Malaga. Campus de Excelencia Internacional Andalucia Tech.
Esta investigacion ha sido parcialmente financiada por CELTIC C2017/2-2 en colaboracion con las empresas EMERGYA y SECMOTIC en los contratos #8.06/5.47.4997 y #8.06/5.47.4996. Tambien agradecemos el apoyo del Ministerio de Economia y Competitividad y de los fondos FEDER, proyectos: TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es) y TIN2017-88213-R (http://6city.lcc.uma.es), y a la Universidad de Malaga. Campus de Excelencia Internacional Andalucia Tech.

Enrique Alba

Papers

Nonlife Loss Reserving

Genetic algorithm for qubits initialisation in noisy intermediate-scale quantum machines: the IBM case study

Predictive Modeling Applications In Actuarial Science: Bayesian Regression Models

Un primer enfoque para medir la calidad de FIWARE

Optimal Location of Antennas in Telecommunication Networks