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Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations

Abstract

A problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations for every player. A form of expert estimations is suggested. The form is of binary type, wherein the expert’s judgment is either 1 or 0. This type is the easiest to be implemented in social networks. For most social networks, 1 can be a “like” (the currently evaluated situation is advantageous), and 0 is a “dislike” (disadvantageous). A method of processing expert estimations is substantiated. Two requirements are provided for obtaining disambiguous payoff averages along with the clustered payoff matrices.

Open access
A Simple Algorithm for Simultaneous Sine Signal Parameters Estimation

This paper presents a new algorithm based on the use of partial derivatives of the processed signal and the weighted estimation procedure to estimate the fundamental frequency, the amplitude and the phase of sinusoidal signal. The proposed algorithm is able to estimate the signal parameters simultaneously, supposing the time-varying frequency. The simulation results verify the effectiveness of the proposed algorithm.

Open access
Selected Robust Methods for Camp Model Estimation

Abstract

This paper presents evidence that Ordinary Least Squares estimators of beta coefficients of major firms and portfolios are highly sensitive to observations of extremes in market index returns. This sensitivity is rooted in the inconsistency of the quadratic loss function in financial theory. By introducing considerations of risk aversion into the estimation procedure using alternative estimators measures of variability we can overcome this lack of robustness and improve the reliability of the results.

Open access
Parameter Identification and Estimation For Stage–Structured Population Models

Abstract

A stage-structured population model with unknown parameters is considered. Our purpose is to study the identifiability of the model and to develop a parameter estimation procedure. First, we analyze whether the parameter vector can or cannot uniquely be determined with the knowledge of the input-output behavior of the model. Second, we analyze how the information in the experimental data is translated into the parameters of the model. Furthermore, we propose a process to improve the recursive values of the parameters when successive observation data are considered. The structure of the state matrix leads to an analysis of the inverse of a sum of rank-one matrices.

Open access
Nonlinear state observers and extended Kalman filters for battery systems

Abstract

The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging.

The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations are directly accessible by measurements. Moreover, this work provides a comparison of the performance of different observer and filtering techniques as well as a development of estimation procedures that guarantee a reliable detection of large parameter variations. For that reason, different charging and discharging current profiles of batteries are investigated by numerical simulations. The estimation procedures considered in this paper are, firstly, a nonlinear Luenberger-type state observer with an offline calculated gain scheduling approach, secondly, a continuous-time extended Kalman filter and, thirdly, a hybrid extended Kalman filter, where the corresponding filter gains are computed online.

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Qualitative and Quantitave Risk Evaluation on the Basis of Military Aviation Event Analysis

Abstract

Risk management is understood as a process supporting decision making through systemic assessment of possible courses of action, identification of hazards and benefits, and indication of the best way to carry out aviation tasks.

The article presents a method of qualitative and quantitative risk assessment on the basis of military aviation incident analysis. The procedure of the suggested approach is contained in a probability and severity degree estimation algorithm and in subsequent steps: of identifying the risk factor (CZN) of a given category, analysis of risk associated with a given CZN, determination of risk, defining correcting actions, risk forecasting and its verification over the following assessment period. A simplified block diagram presents a proposal of the implementation of a qualitative and quantitative risk estimation procedure with the use of the TURAWA IT system, which function in the air forces.

Open access
Three contributions to robust regression diagnostics

Abstract

Robust regression methods have been developed not only as a diagnostic tool for standard least squares estimation in statistical and econometric applications, but can be also used as self-standing regression estimation procedures. Therefore, they need to be equipped by their own diagnostic tools. This paper is devoted to robust regression and presents three contributions to its diagnostic tools or estimating regression parameters under non-standard conditions. Firstly, we derive the Durbin-Watson test of independence of random regression errors for the regression median. The approach is based on the approximation to the exact null distribution of the test statistic. Secondly, we accompany the least trimmed squares estimator by a subjective criterion for selecting a suitable value of the trimming constant. Thirdly, we propose a robust version of the instrumental variables estimator. The new methods are illustrated on examples with real data and their advantages and limitations are discussed.

Open access
Mathematical Models for Dynamics of Molecular Processes in Living Biological Cells. A Single Particle Tracking Approach

Abstract

In this survey paper we present a systematic methodology of how to identify origins of fractional dynamics. We consider three models leading to it, namely fractional Brownian motion (FBM), fractional Lévy stable motion (FLSM) and autoregressive fractionally integrated moving average (ARFIMA) process. The discrete-time ARFIMA process is stationary, and when aggregated, in the limit, it converges to either FBM or FLSM. In this sense it generalizes both models. We discuss three experimental data sets related to some molecular biology problems described by single particle tracking. They are successfully resolved by means of the universal ARFIMA time series model with various noises. Even if the finer details of the estimation procedures are case specific, we hope that the suggested checklist will still have been of great use as a practical guide. In Appendices A-F we describe useful fractional dynamics identification and validation methods.

Open access
Estimating R&D Returns In Health Care Industry

Abstract

Purpose: This paper provides an empirical evaluation of R&D returns for a series of global companies who lead in innovation within the health care industry.

Methodology: The estimation procedure bases on two specifications: the parametric production function setting, and finite distributed lag model (FDL).

Findings: Using the most recent data on R&D investment by health care equipment and services along pharmaceuticals and biotechnology companies, we confirm the positive albeit mitigated impact of R&D efforts on performance indicators (levels of sales). Moreover, we comment on the current phenomena observed in health care industry and offer a policy view for ongoing and future challenges in the sector.

Added value: Since there is a dearth of recent empirical evidence on R&D returns in the broad health industry, this paper offers the evaluation of economic incentives for companies to invest in R&D. These incentives embrace the induced increase in sales and profits levels. The authors participate in a public debate concerning the optimal levels of R&D rewards required to sustain the innovation within the sector.

Open access
Estimation of Suspended Sediment Concentrations with Adcp in Danube River

Abstract

An estimation procedure for suspended sediment concentrations based on the intensity of backscattered sound of acoustic Doppler current profilers (ADCP) is introduced in this paper. Based on detailed moving and fixed boat ADCP measurements with concurrent sediment sampling, we have successfully calibrated the estimation method for a reach of River Danube in Hungary, characterized by significant suspended sediment transport. The effect of measurement uncertainty and various data filtering on sediment load determination is also analyzed and quantified. Some of the physical model parameters describing the propagation of sound in water are estimated based on known empirical formulas, while other parameters are derived from measured. Regression analysis is used to obtain a relationship between the intensity of backscattered sound and sediment concentrations. The empirical relationship has been then used to estimate the suspended sediment concentrations from the ADCP data collected in fixed and moving boat measurement operation mode, along verticals and path-lines, respectively. We show that while some measurement uncertainty is inherent to the acoustic Doppler principle, it is further enhanced by the complexity of the near-bottom sediment-laden flow. This uncertainty has then a significant effect on the local sediment load estimation. In turn, reasonable smoothing of raw velocity and backscatter intensity data shows insignificant impact on cross-sectional sediment load estimation.

Open access