The problem of optimally controlling a Wiener process until it leaves an interval (a; b) for the first time is considered in the case when the infinitesimal parameters of the process are random. When a = ��1, the exact optimal control is derived by solving the appropriate system of differential equations, whereas a very precise approximate solution in the form of a polynomial is obtained in the two-barrier case.
Usually, cellular networks are modeled by placing each tier (e.g macro, pico and relay nodes) deterministically on a grid. When calculating the metric performances such as coverage probability, these networks are idealized for not considering the interference. Overcoming such limitation by realistic models is much appreciated. This paper considered two- tier twohop cellular network, each tier is consisting of two-hop relay transmission, relay nodes are relaying the message to the users that are in the cell edge. In addition, the locations of the relays, base stations (BSs), and users nodes are modeled as a point process on the plane to study the two hop downlink performance. Then, we obtain a tractable model for the k-coverage probability for the heterogeneous network consisting of the two-tier network. Stochastic geometry and point process theory have deployed to investigate the proposed two-hop scheme. The obtained results demonstrate the effectiveness and analytical tractability to study the heterogeneous performance.
A description of direct simulation of crosswind loads caused by critical vortex excitation and the response of the structure to these loads are presented in this paper. Tower-like structures of circular cross-sections are considered. A proposed mathematical model of vortex excitation has been numerically implemented and a selfserving computer program was created for the purpose. This software, cooperating with the FEM system, allows for a simulation of a crosswind load and lateral response in real time, meaning that at each time step of the calculations the load is generated using information regarding displacements seen beforehand. A detailed description of the mathematical model is neglected in this paper, which is focused on numerical simulations. WAWS and AR methods are used in simulations.
The draw theory is the foundation for decreasing ore loss and dilution indices while extracting deposits from mines. Therefore, research on draw theory is of great significance to optimally guide the draw control and improve the economy efficiency of mines. The laboratory scaled physical draw experiments under inclined wall condition conducted showed that a new way was proposed to investigate the flow zone of granular materials. The flow zone was simply divided into two parts with respect to the demarcation point of the flow axis. Based on the stochastic medium draw theory, theoretical movement formulas were derived to define the gravity flow of fragmented rocks in these two parts. The ore body with 55° dip and 10 m width was taken as an example, the particle flow parameters were fitted, and the corresponding theoretical shape of the draw body was sketched based on the derived equation of draw-body shape. The comparison of experimental and theoretical shapes of the draw body confirmed that they coincided with each other; hence, the reliability of the derived equation of particle motion was validated.
The paper aims at comparing forecast ability of VAR/VEC models with a non-changing covariance matrix and two classes of Bayesian Vector Error Correction – Stochastic Volatility (VEC-SV) models, which combine the VEC representation of a VAR structure with stochastic volatility, represented by the Multiplicative Stochastic Factor (MSF) process, the SBEKK form or the MSF-SBEKK specification. Based on macro-data coming from the Polish economy (time series of unemployment, inflation and interest rates) we evaluate predictive density functions employing of such measures as log predictive density score, continuous rank probability score, energy score, probability integral transform. Each of them takes account of different feature of the obtained predictive density functions.
This paper provides analyses of the accuracy and convergence time of the PPP method using GPS systems and different IGS products. The official IGS products: Final, Rapid and Ultra Rapid as well as MGEX products calculated by the CODE analysis centres were used. In addition, calculations with weighting function of the observations were carried out, depending on the elevation angle. The best results were obtained for CODE products, with a 5-minute interval precision ephemeris and precise corrections to satellite clocks with a 30-second interval. For these calculations the accuracy of position determination was at the level of 3 cm with a convergence time of 44 min. Final and Rapid products, which were orbit with a 15-minute interval and clock with a 5 minute interval, gave very similar results. The same level of accuracy was obtained for calculations with CODE products, for which both precise ephemeris and precise corrections to satellite clocks with the interval of 5 minutes. For these calculations, the accuracy was 4 cm with the convergence time of 70 min. The worst accuracy was obtained for calculations with Ultra-rapid products, with an interval of 15 minutes. For these calculations, the accuracy was 10 cm with a convergence time of 120 min. The use of the weighting function improved the accuracy of position determination in each case, except for calculations with Ultra-rapid products. The use of this function slightly increased the convergence time, in addition to the CODE calculation, which was reduced to 9 min.
In the paper the analysis of random vibration of an actively damped laminated plate with functionally graded piezoelectric actuator layers is presented. The simply supported plate is subjected to stochastic loading represented by a uniformly distributed pressure. The random input is assumed as a Gaussian sta- tionary and ergodic process. The actuators are regarded as a multi-layer structure arranged of piezofiber composite sub-layers. The sub-layers differ each other with amount of PZT (lead-zirconate-titanate) fibers and are stacked to achieve a desired change of the PZT volume fraction through the actuator thickness. The gradation scheme of constituents and material properties are estimated by parabolic and power functions. Numerical simulations are performed to recognize the influence of the applied random excita- tions and the actuator properties gradations on the characteristics of the stochastic field of active plate deflection i.e. power spectral density, autocorrelation function and variance
This article aims at constructing a new method for testing the statistical significance of seasonal fluctuations for non-stationary processes. The constructed test is based on a method of subsampling and on the spectral theory of Almost Periodically Correlated (APC) time series. In the article we consider an equation of a nonstationary process, containing a component which includes seasonal fluctuations and business cycle fluctuations, both described by an almost periodic function. We build subsampling test justifying the significance of frequencies obtained from the Fourier representation of the unconditional expectation of the process. The empirical usefulness of the constructed test is examined for selected macroeconomic data. The article studies survey indicators of economic climate in industry, retail trade and consumption for European countries.
In the paper finite-dimensional time-variable dynamical control systems described by linear stochastic ordinary differential state equations with single time-variable point delay in the control are considered. Using notations, theorems and methods taken directly from deterministic controllability problems necessary and sufficient conditions for different kinds of stochastic relative controllability in a given time interval are formulated and proved. It will be proved that under suitable assumptions relative controllability of a deterministic linear associated dynamical system is equivalent to stochastic relative exact controllability and stochastic relative approximate controllability of the original linear stochastic dynamical system. Some remarks and comments on the existing results for stochastic controllability of linear dynamical systems are also presented.
This paper presents some new results on exogeneity in models with latent variables. The concept of exogeneity is extended to the class of models with latent variables, in which a subset of parameters and latent variables is of interest. Exogeneity is discussed from the Bayesian point of view. We propose sufficient weak and strong exogeneity conditions in the vector error correction model (VECM) with stochastic volatility (SV) disturbances. Finally, an empirical illustration based on the VECM-SV model for the daily growth rates of two main official Polish exchange rates: USD/PLN and EUR/PLN, as well as EUR/USD from the international Forex market is presented. The exogeneity of the EUR/USD rate is examined. The strong exogeneity hypothesis of the EUR/USD rate is not rejected by the data.
The paper discusses Bayesian productivity analysis of 27 EU Member States, USA, Japan and Switzerland. Bayesian Stochastic Frontier Analysis and a two-stage structural decomposition of output growth are used to trace sources of output growth. This allows us to separate the impacts of capital accumulation, labour growth, technical progress and technical efficiency change on economic development. Since estimates of the growth components are conditioned upon model parameterisation and the underlying assumptions, a number of possible specifications are considered. The best model for decomposing output growth is chosen based on the highest marginal data density, which is calculated using adjusted harmonic mean estimator.
The main aim of this paper is to analyse the effect of Common Agricultural Policy (CAP) subsidies on technical efficiency of Polish dairy farms. We have distinguished several types of subsidies and provided an analysis to find out which types are most likely to engender systematic differences in technical efficiency. A balanced panel of microeconomic data on Polish dairy farms over an eight-year period (between 2004 and 2011), taken from the Farm Accountancy Data Network (FADN), is used. The translog production function is estimated by employing the Bayesian approach. The empirical results show that the elasticity of production with respect to livestock is the highest, whereas with respect to feed is the lowest. The mean technical efficiency in the covered period is 83%. The research reveals the negative effect of subsidies on technical efficiency.
In this work, a fast 32-bit one-million-channel time interval spectrometer is proposed based on field programmable gate arrays (FPGAs). The time resolution is adjustable down to 3.33 ns (= T, the digitization/discretization period) based on a prototype system hardware. The system is capable to collect billions of time interval data arranged in one million timing channels. This huge number of channels makes it an ideal measuring tool for very short to very long time intervals of nuclear particle detection systems. The data are stored and updated in a built-in SRAM memory during the measuring process, and then transferred to the computer. Two time-to-digital converters (TDCs) working in parallel are implemented in the design to immune the system against loss of the first short time interval events (namely below 10 ns considering the tests performed on the prototype hardware platform of the system). Additionally, the theory of multiple count loss effect is investigated analytically. Using the Monte Carlo method, losses of counts up to 100 million events per second (Meps) are calculated and the effective system dead time is estimated by curve fitting of a non-extendable dead time model to the results (τNE = 2.26 ns). An important dead time effect on a measured random process is the distortion on the time spectrum; using the Monte Carlo method this effect is also studied. The uncertainty of the system is analysed experimentally. The standard deviation of the system is estimated as ± 36.6 × T (T = 3.33 ns) for a one-second time interval test signal (300 million T in the time interval).
In this paper the basic methodology of the coupled response-degradation modelling of stochastic dynamical systems is presented along with the effective analysis of selected problems. First, the general formulation of the problems of stochastic dynamics coupled with the evolution of deterioration process is given. Then some specific degrading oscillatory systems under random excitation are analyzed with a special attention on the systems with fatigue-induced stiffness degradation. Both, the general discussion and the analysis of selected exemplary problems indicate how the reliability of deteriorating stochastic dynamical systems can be assessed.
The supply chain of spare parts is the intersection between the supply chain, the after-sales and the maintenance services. Some authors have tried to define improvement paths in terms of models to satisfy the performance criteria. In addition, other authors are directed towards the integration of risk management in the demand forecasting and the stock management (performance evaluation) through probabilistic models. Among these models, the probabilistic graphical models are the most used, for example, Bayesian networks and petri nets. Performance evaluation is done through performance indicators. To measure the appreciation of the supply of the spare parts stock, this paper focuses on the performance evaluation of the system by petri nets. This evaluation will be done through an analytical study. The purpose of this study is to evaluate and analyze the performance of the system by proposed indicators. First, we present a literature review on Petri nets which is the essential tool in our modeling. Secondly, we present in the third section the analytical study of the model based on bath deterministic and stochastic petri networks. Finally, we present an analysis of the proposed model compared to the existing ones.
We discuss the empirical importance of long term cyclical effects in the volatility of financial returns. Following Amado and Teräsvirta (2009), ČiŽek and Spokoiny (2009) and others, we consider a general conditionally heteroscedastic process with stationarity property distorted by a deterministic function that governs the possible time variability of the unconditional variance. The function proposed in this paper can be interpreted as a finite Fourier approximation of an Almost Periodic (AP) function as defined by Corduneanu (1989). The resulting model has a particular form of a GARCH process with time varying parameters, intensively discussed in the recent literature. In the empirical analyses we apply a generalisation of the Bayesian AR(1)-GARCH model for daily returns of S&P500, covering the period of sixty years of US postwar economy, including the recently observed global financial crisis. The results of a formal Bayesian model comparison clearly indicate the existence of significant long term cyclical patterns in volatility with a strongly supported periodic component corresponding to a 14 year cycle. Our main results are invariant with respect to the changes of the conditional distribution from Normal to Student-tand to the changes of the volatility equation from regular GARCH to the Asymmetric GARCH.
The main focus of this tutorial/review is on presenting Prospect Theory in the context of the still ongoing debate between the behavioral (mainly descriptive) and the classical (mainly normative) approach in decision theory under risk and uncertainty. The goal is to discuss Prospect Theory vs. Expected Utility in a comparative way. We discuss: a) which assumptions (implicit and explicit) of the classical theory are being questioned in Prospect Theory; b) how does the theory incorporate robust experimental evidence, striving, at the same time, to find the right balance between the basic rationality postulates of Expected Utility (e.g. monotonicity wrt. First-Order Stochastic Dominance), psychological plausibility and mathematical elegance; c) how are risk attitudes modeled in the theory. In particular we discuss prospect stochastic dominance and the three-pillar structure of modeling risk attitudes in Prospect Theory involving: the non-additive decision weights with lower and upper subadditivity and their relationship to the notions of pessimism and optimism, as well as preferences towards consequences separated into preferences within and across the domains of gains and losses (corresponding to basic utility and loss aversion), d) example applications of Prospect Theory.
In the paper finite-dimensional stationary dynamical control systems described by linear stochastic ordinary differential state equations with single point delay in the control are considered. Using notations, theorems and methods taken directly from deterministic controllability problems, necessary and sufficient conditions for different kinds of stochastic relative controllability are formulated and proved. It will be proved that under suitable assumptions relative controllability of a deterministic linear associated dynamical system is equivalent to stochastic relative exact controllability and stochastic relative approximate controllability of the original linear stochastic dynamical system. Some remarks and comments on the existing results for stochastic controllability of linear dynamical systems with delays are also presented. Finally, minimum energy control problem for stochastic dynamical system is formulated and solved.
The study aims at a statistical verification of breaks in the risk-return relationship for shares of individual companies quoted at the Warsaw Stock Exchange. To this end a stochastic volatility model incorporating Markov switching in-mean effect (SV-MS-M) is employed. We argue that neglecting possible regime changes in the relation between expected return and volatility within an ordinary SV-M specification may lead to spurious insignificance of the risk premium parameter (as being ’averaged out’ over the regimes).Therefore, we allow the volatility-in-mean effect to switch over different regimes according to a discrete homogeneous two- or three-state Markov chain. The model is handled within Bayesian framework, which allows to fully account for the uncertainty of model parameters, latent conditional variances and state variables. MCMC methods, including the Gibbs sampler, Metropolis-Hastings algorithm and the forward-filtering-backward-sampling scheme are suitably adopted to obtain posterior densities of interest as well as marginal data density. The latter allows for a formal model comparison in terms of the in-sample fit and, thereby, inference on the ’adequate’ number of the risk premium regime
The model considered in the paper is defined as VAR with the prior distribution for parameters generated by the dynamic stochastic general equilibrium (DSGE) model. The degree of economic restrictions in the DSGE-VAR model is controlled by the weighting parameter. In the paper there is investigated the impact of the weighting parameter prior specifications for the posterior shape of impulse response functions (IRFs). In case of conditional models the paths of IRFs highly depend on the value of the weighting parameter that is set arbitrary. When considering full estimation with different prior types, means and gradual change in the dispersion the posterior time paths of IRFs are similar in models with high values of the marginal data density.
The main idea of this work is to demonstrate an application of the generalized perturbation-based Stochastic Finite Element Method for a determination of the reliability indicators concerning elastic stability for a certain spectrum of the civil engineering structures. The reliability indicator is provided after the Eurocode according to the First Order Reliability Method, and computed using the higher order Taylor expansions with random coefficients. Computational implementation provided by the hybrid usage of the FEM system ROBOT and the computer algebra system MAPLE enables for reliability analysis of the critical forces in the most popular civil engineering structures like simple Euler beam, 2 and 3D single and multi-span steel frames, as well as polyethylene underground cylindrical shell. A contrast of the perturbation-based numerical approach with the Monte-Carlo simulation technique for the entire variability of the input random dispersion included into the Euler problem demonstrates the probabilistic efficiency of the perturbation method proposed.
The paper considers the modeling and estimation of the stochastic frontier model where the error components are assumed to be correlated and the inefficiency error is assumed to be autocorrelated. The multivariate Farlie-Gumble-Morgenstern (FGM) and normal copula are used to capture both the contemporaneous and the temporal dependence between, and among, the noise and the inefficiency components. The intractable multiple integrals that appear in the likelihood function of the model are evaluated using the Halton sequence based Monte Carlo (MC) simulation technique. The consistency and the asymptotic efficiency of the resulting simulated maximum likelihood (SML) estimators of the present model parameters are established. Finally, the application of model using the SML method to the real life US airline data shows significant noise-inefficiency dependence and temporal dependence of inefficiency.
A Bayesian stochastic volatility model with a leverage effect, normal errors and jump component with the double exponential distribution of a jump value is proposed. The ready to use Gibbs sampler is presented, which enables one to conduct statistical inference. In the empirical study, the SVLEDEJ model is applied to model logarithmic growth rates of one month forward gas prices. The results reveal an important role of both jump and stochastic volatility components.
A study on plug-in electric vehicle (PEV) charging load and its impacts on distribution transformers loss-of-life, is presented in this paper. The assessment is based on residential PEV battery charging. As the exact forecasting of the charging load is not possible, the method for predicting the electric vehicle (EV) charging load is stochastically formulated. With the help of the stochastic model, the effect of fixed, time of use, and real-time charging rates on the charging load and the resultant impact on transformer derating is investigated. A 38-bus test system is adopted as the test system including industrial harmonic sources. Test results demonstrate that uncontrolled EV charging might causes a noticeable change in the K-factor of the transformer, emerging the need for derating, while applying real-time rates for battery charging loads conquers this problem even in case of harmonic-rich chargers.