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

A globally convergent augmented lagrangian algorithm for optimization with general constraints and simple bounds

Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing

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Somewhere over the rainbow

The idea of a methodology capable of determining in a precise and practical way the optimal sample size came from studying Monte Carlo simulation models concerning financial problems, risk analysis, and supply chain forecasting. In these cases the number of extractions from the frequency distributions characterizing the model is inadequate or limited to just one, so it is necessary to replicate simulation runs many times in order to obtain a complete statistical description of the model variables. Generally, as shown in the literature, the sample size is fixed by the experimenter based on empirical assumptions without considering the impact on result accuracy in terms of tolerance interval.
In this paper, the authors propose a methodology by means of which it is possible to graphically highlight the evolution of experimental error variance as a function of the sample size. Therefore, the experimenter can choose the best ratio between the experimental cost and the expected results.

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Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size

In literature, the applications of simulation to the stochastic analysis of investments do not often give a satisfactory result to, at least, two problems that can condition in a determinant way the validity of the analysis made. In synthesis: 1. no knowledge of the entity of the experimental error expressed in terms of Mean Square Pure Error (MSpe)

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The Stochastic Analysis of Investments in Industrial Plants by Simulation Models with Control of Experimental Error: Theory and Application to a Real Business Case

The global financial crisis of 2007-2008 has highlighted the importance of a correct pricing of the so-called financial derivatives. Analyzing the methodology of pricing of non-listed derivatives by using the Monte 6168 Ilaria Bendato et al. Carlo method, the Authors have realized that the determination of the sample size is not managed properly. This is because the research offices of banks rely on, as suggested by the literature of the field and technical manuals for practitioners, a standard number of simulation runs, by rules of thumb, between 1,000 and 10,000. The consequence is that financial institutions lead to financial statements fair values with no knowledge of its fluctuation band and the robustness of the result. Conscious of this practice, the Authors, dealing from a long time to the topic of output reliability in applications of discrete event simulation and Monte Carlo simulation, address the problem through the use of a methodology based on the control of Mean Pure Square Error (MSPE), already successfully tested in other contexts. Thanks to the proposed approach, applied for pricing complex derivatives, it is possible to determine the size of the experimental sample in order to ensure a pre-assigned degree of reliability of the output results.

Monte Carlo method for pricing complex financial derivatives: An innovative approach to the control of convergence

In the latest times, power markets in Europe, including the Spanish one called MIBEL (Mercado Iberico de Electricidad), are being deregulated and coupled. As a result, electricity can be easily purchased and sold across further areas and countries. On the other hand, trying to guarantee renewable projects profitability, Power Purchase Agreements and Options contracts are arising as a feasible solution. The problem arises when the power plant owners have to negotiate the purchase electricity price in order to optimize risks and profits, as well as make future plans. Thus, several methods for Electricity Price Forecasting (EPF) have been developed and presented, showing different results, as market spot prices suffer from strong seasonality, spikes and high volatility. In this paper, three methods, based on Deep Learning Dynamic Neural Networks (NAR, NARX and LSTM) applied to forecast MIBEL electricity spot prices are discussed in order to evaluate their adequacy, accuracy and reliable horizon.

Electricity Spot Prices Forecasting for MIBEL by using Deep Learning: a comparison between NAR, NARX and LSTM networks

This paper aims to examine how the radial basis function (RBF) technique works in the financial field, to compare the RBF performance with the results obtained with traditional methods (FDM, FEM), to choose the more suitable radial basis function to solve option pricing and to explain how its shape parameters can be set. It is crucial to set properly the shape parameter for the precision of the method and ultimately for the determination of the derivatives fair-value. Applying a maximum likelihood estimation (MLE), the authors propose a financial approach for its evaluation based on market/theoretical prices calibration.

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Option pricing via radial basis functions: Performance comparison with traditional numerical integration scheme and parameters choice for a reliable pricing

Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design m...

Pier Giuseppe Giribone

Papers

The Stochastic Analysis of Investments in Industrial Plants by Simulation Models with Control of Experimental Error: Theory and Application to a Real Business Case

Monte Carlo method for pricing complex financial derivatives: An innovative approach to the control of convergence

Electricity Spot Prices Forecasting for MIBEL by using Deep Learning: a comparison between NAR, NARX and LSTM networks

Option pricing via radial basis functions: Performance comparison with traditional numerical integration scheme and parameters choice for a reliable pricing

Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals