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1
Content available remote

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

100%
Boran S., Diren D.
Acta Physica Polonica A
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2017
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vol. 132
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issue 3
1054-1057
EN
Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. In this study, the proposed model is expected to detect the source(s) for out-of-control signals without help of an expert in the process, by using a multilayer neural network. This model was implemented in furniture fasteners manufacturing. Time gain was obtained while detecting source(s) for out-of-control signals.
2
Content available remote

Performance of Some Robust Estimators for the Weibull Distribution

100%
Gündüz N., Başar E., Aydin C.
Acta Physica Polonica A
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2015
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vol. 128
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issue 2B
B-203-B-207
EN
In this study, we consider some of univariate quantile-based robust estimators. We focus on the estimators such as median, interquartile range, quartile and octile skewness for the Weibull distribution which is one of the most widely applied probability function because of its versatility and relative simplicity. It is important to use robust estimators as a measure of distribution properties for analyzing data in the case of contamination with outliers. For small data sets, it is reported that by introducing kernel estimation for smoothing empirical distribution function, a reduction in mean square error of estimator is achieved by Fernholz (1997) and Hubert et al. (2013). In kernel estimation, it is well known that bandwidth selection is more important than selection of kernel density since bandwidth controls the smoothness of the estimated distribution function. Using simulation studies, we examine some quantile-based estimators for the Weibull distribution with various sample size. The performance of estimators is measured by mean squared error under Different outlier contaminated data. We applied this idea in the case of real data.
3
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A Bias Reducing Approach for Some Robust Estimators by Predicting Roughness in Case of Kernel Estimation

100%
Gündüz N., Aydın C., Başar E.
Acta Physica Polonica A
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2016
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vol. 130
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issue 1
422-427
EN
In the density estimation it is known that estimators are heavily biased. We applied a bias reducing approach to improve some quantile estimators for Weibull distribution having different parameter values and contamination level. In this study, we estimate the bias for any quantile value and obtained biased reduced smoothed distribution function by simulation study for random samples of size 40. Then, the mean square error of some robust quantile estimators and variances are obtained from biased reduced smoothed distribution function. Furthermore, we obtained sampling distribution of roughness and sampling distribution of estimated bias related some quantile estimators.
4
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Generalized Linear Models for European Union Countries Energy Data

80%
İyıt N., Yonar H., Genç A.
Acta Physica Polonica A
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2016
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vol. 130
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issue 1
397-400
EN
The class of generalized linear models is an extension of traditional linear models that allows the mean of the response variable to be linearly dependent on the explanatory variables through a link function. Generalized linear models allow the probability distribution of the response variable to be a member of an exponential family of distributions. The exponential family of distributions include many common discrete and continuous distributions such as normal, binomial, multinomial, negative binomial, Poisson, gamma, inverse Gaussian, etc. Also link functions can be built as identity, logit, probit, power, log, and complementary log-log link functions. In this study, supply, transformation and consumption, imports and exports of solid fuels, oil, gas, electricity, and renewable energy annual data of European Union countries between 2005 and 2013 years are investigated by using generalized linear models. In this case, the response variable is taken as annual complete energy balances of European Union countries as a continuous variable having positive values, and the distribution of the response variable comes from the gamma distribution with log-link function.
5
Content available remote

Quantitative Characteristics of Correlations of Meteorological Data

80%
Rak R., Bwanakare S.
Acta Physica Polonica A
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2016
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vol. 129
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issue 5
922-926
EN
This paper presents the quantitative characteristics of correlations (and cross-correlations) of plant main eco-factors i.e. the ground and over-ground temperature, the wind speed, and the humidity. The study is based upon hourly data statistical observations collected in the region of Lublin, in Poland for the period 2001.05.07-2009.04.10. This paper indicates that plant growth conditions constitute an emergent response to the above direct eco-factors. Then, the dynamics properties of each eco-factor is first analyzed alone for its multifractal structure. We apply the multifractal detrended correlation analysis and multifractal detrended cross-correlation analysis. We show that the widest multifractal spectrum is for over-ground temperature and the strongest power-law cross-correlations exist between ground and over-ground temperature. Next, an impulse response analysis is carried out to measure dynamical inter causalities within all the considered variables. As far as cross-impact between different eco-variables is concerned, one observes that the wind speed, the ground temperature and the air humidity dynamics are the most influenced, in terms of memory length time, by external temperature.
6
Content available remote

Chaos in the Brain

80%
Kříž J.
Acta Physica Polonica A
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2011
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vol. 120
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issue 6A
A-127-A-131
EN
We describe several links between EEG data processing and quantum mechanics. Then we show examples of exploitation of methods commonly used in quantum chaos for EEG data analysis.
7
Content available remote

Comprehensive Analyses of Gaussian Graphical Model under Different Biological Networks

80%
Dokuzoğlu D., Purutçuoğlu V.
Acta Physica Polonica A
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2017
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vol. 132
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issue 3
1106-1111
EN
Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be shown by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this probabilistic model via different criteria, from the change in dimension of the systems to the change in the distribution of the data. Hereby, we generate high dimensional simulated datasets via copulas and apply them in Gaussian graphical model to compare sensitivity, specificity, F-measure and various other accuracy measures. We also assess its performance under real datasets. We consider that such comprehensive analyses can be helpful for assessing the limitation of this common model and for developing alternative approaches, to overcome its disadvantages.
8
Content available remote

DMRG Approach to a Molecular-Based Bimetallic Chain Containing Re(IV) and Cu(II) Ions

80%
Sobczak P., Barasiński A., Matysiak R., Drzewiński A., Kamieniarz G., Bieńko A., Mroziński J.
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issue 5
975-977
EN
The bimetallic chain complex [Cu(tren)]ReCl_6 is numerically analysed on the basis of the anisotropic quantum Heisenberg model without the mean-field corrections by the density-matrix renormalization group approach. The high accuracy results of our simulations have been fitted to the corresponding experimental susceptibility data above the crossover regime. The set of model parameters comprising the strength of antiferromagnetic couplings, the single-ion anisotropy term and the corresponding g factors have been found: J/k_{B} = 3.5 ± 0.5 K, D/k_{B} = 35 ± 5 K, g_{Cu} = 2.07 ± 0.05 and g_{Re} = 1.73 ± 0.01.
9
Content available remote

Student's t-Distribution versus Zeldovich-Kompaneets Solution of Diffusion Problem

80%
Wojnar R.
Acta Physica Polonica A
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2012
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vol. 121
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issue 2B
B-133-B-136
EN
Student's t-distribution is compared to a solution of superdiffusion equation. This t-distribution is a continuous probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small. Formally it can written in the form similar to the Gaussian distribution, in which, however, instead of usual exponential function, the so called K-exponential - a form of binomial distribution - appears. Similar binomial form has the Zeldovich-Kompaneets solution of nonlinear diffusion-like problems. A superdiffusion process, similar to a Zeldovich-Kompaneets heat conduction process, is defined by a nonlinear diffusion equation in which the diffusion coefficient takes the form $D=a(t)(1/f)^n$, where a=a(t) is an external time modulation, n is a positive constant, and f=f(x,t) is a solution to the nonlinear diffusion equation. It is also shown that a Zeldovich-Kompaneets solution still satisfies the superdiffusion equation if a=a(t) is replaced by the mean value of a. A solution to the superdiffusion equation is given. This may be useful in description of social, financial, and biological processes. In particular, the solution possesses a fat tail character that is similar to probability distributions observed at stock markets. The limitation of the analogy with the Student distribution is also indicated.
10
Content available remote

Granger Causality and Transfer Entropy for Financial Returns

70%
Syczewska E., Struzik Z.
Acta Physica Polonica A
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2015
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vol. 127
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issue 3A
A-129-A-135
EN
Granger causality in its linear form has been shown by Barnett, Barrett and Seth [Phys. Rev. Lett. 103, 238701 (2009)] to be equivalent to transfer entropy in case of Gaussian distribution. Generalizations by Hlaváčková-Schindler [Appl. Math. Sci. 5, 3637 (2011)] are applied to distributions typical for biomedical applications. The financial returns, which are of great importance in financial econometrics, typically do not have Gaussian distribution. Generalizations leading to the concept of nonlinear Granger causality (e.g. causality in variance, causality in risk), known and applied in econometric literature, seem to be less known outside this field. In the paper an overview of some of the definitions and applications is given. In particular, we indicate some recent econometric results concerning application of the tests in linear multivariate framework. We emphasize importance of other variants of Granger causality, and need of development of methods reflecting features of financial variables.
11
Content available remote

Asymmetry Coefficients as Indicators of Chaos: Hyperchaotic Qi System

70%
Wąż P., Bielińska-Wąż D.
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EN
Statistical properties of the hyperchaotic Qi system are studied. The theory, recently formulated and applied for the damped driven pendulum, is used in this investigation. Asymmetry coefficients, related to the statistical moments of distributions composed from the time-series, are shown to behave in a different way for periodic, chaotic and hyperchaotic solutions and are proposed as indicators of chaos and hyperchaos.
12
Content available remote

Asymmetry Coefficients as Indicators of Chaos

70%
Wąż P., Bielińska-Wąż D.
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EN
The aim of this paper is to present a new simple indicator of chaos derived from the dynamics of the motion. For this purpose statistical methods are used. A function describing the motion of the analyzed system (in the example under consideration, the time dependence of the angle of a damped driven pendulum, ω(t)) is recorded in time intervals t∊〈 T_{s}, T_{f_{k}}〉, k = 1, 2,...K, with T_{f_{k}} > T_{f_{k-1}}. Each of the recorded functions is considered as a statistical distribution. The asymmetry coefficients of the set of distributions form a series and their behavior in periodic and chaotic regions is compared. It is shown that the behavior of this series in the chaotic and in the periodic regimes is entirely different. The changes of the asymmetry coefficients for the periodic cases are very regular and for the chaotic ones - random. In periodic cases, the coefficients converge to zero when the length of the distribution increases.
13
Content available remote

Prediction of the Probabilities of the Transmission of Genetic Traits within Bayesian Logical Inference

61%
Cevri M., Üstündağ D.
Acta Physica Polonica A
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2016
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vol. 130
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issue 1
45-50
EN
Predictions for the transmission of genetic traits along to generations are an important process for patients, their family and genetic counseling. For this purpose, Bayesian analysis in which one can include a priori knowledge taking into account all relevant information into the problem could be a useful tool to examine how disease forecasting affects its probability so that it provides a more straightforward interpretation of predictions. Therefore, we investigate here transmissions of autosomal recessive diseases along to generations within Bayesian framework. In order to do that we develop a computer code that is useful to facilitate genetic transition matrices to forecast predictions of probabilities of transmission of genetic traits by using Mathematica software, well known as an algebraic manipulation language. Furthermore, the symbolic implementation of the code is applied for the cystic fibrosis disease forecasting in humans genetics. All results show that Bayesian analysis plays a central role of prediction for probabilities of transmissions of genetic traits along generations for cystic fibrosis disease or other autosomal recessive disorders.
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