Identification of the multiscale fractional Brownian motion with biomechanical applications

In certain applications, for instance biomechanics, turbulence, finance, or Internet traffic, it seems suitable to model the data by a generalization of a fractional Brownian motion for which the Hurst parameter $H$ is depending on the frequency as a piece-wise constant function. These processes are called multiscale fractional Brownian motions. In this contribution, we provide a statistical study of the multiscale fractional Brownian motions. We develop a method based on wavelet analysis. By using this method, we find initially the frequency changes, then we estimate the different parameters and afterwards we test the goodness-of-fit. Lastly, we give the numerical algorithm. Biomechanical data are then studied with these new tools

Filtered derivative with p-value method for multiple change-points detection

This paper deals with off-line detection of change points for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis like method with linear time and memory complexity. Our method is based at first step, on Filtered Derivative method which detects the right change points but also false ones. We improve Filtered Derivative method by adding a second step in which we compute the p-values associated to each potential change points. Then we eliminate as false alarms the points which have p-value smaller than a given critical level. Next, our method is compared with the Penalized Least Square Criterion procedure on simulated data sets. Eventually, we apply Filtered Derivative with p-Value method to segmentation of heartbeat time series

Simplicity of twin tree lattices with non-trivial commutation relations

We prove a simplicity criterion for certain twin tree lattices. It applies to all rank two Kac-Moody groups over finite fields with non-trivial commutation relations, thereby yielding examples of simple non-uniform lattices in the product of two trees

The interplay of uncertainty, structure and trust on the diffusion of management accounting and control systems: An agent based modeling approach

This study attempts to investigate the interplay of uncertainty, structure and trust on the diffusion of a subset of management information systems, namely management accounting and control systems. The article suggests that under conditions of uncertainty, trust and structure are significantly associated with the success of the implementation process. On the other hand, the importance of trust and structure is less significant when the management accounting and control system is not perceived as threatening to organizational actors. The study draws on social network theory and proposes an agent based modeling approach to study the interplay of uncertainty, trust and structure on the diffusion process.

On time preference, rational addiction and utility satiation

A basic consumer problem with a unique good is considered, current consumption of this good influencing in a positive manner consumer intertemporal utility, while past consumption exerts a negative influence. Moreover, in the line of Fisher, a specification of preferences is retained so that the rate of time preference, assumes a long-run value – this means for a stationary consumption-path – that is non-monotonic as a function of consumption: impatience increases for low level of consumptions but decreases for higher ones. Such a framework allows for an integrated appraisal of addiction, satiation and the rate of time preference. It is shown that the emergence of an addiction phenomenon in the neighbourhood of an unsatiated long-run position exactly corresponds to letting the rate of time preference be an increasing function of past consumption habits. When addiction becomes sufficiently strong, the unsatiated stationary state becomes unstable and the satiated steady state becomes the only admissible stationary position.Impatience; Consumption habits; Rational addiction; Satiation

Off-line detection of multiple change points with the Filtered Derivative with p-Value method

This paper deals with off-line detection of change points for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis like method with linear time and memory complexity. Our method is based at first step, on Filtered Derivative method which detects the right change points but also false ones. We improve Filtered Derivative method by adding a second step in which we compute the p-values associated to each potential change points. Then we eliminate as false alarms the points which have p-value smaller than a given critical level. Next, our method is compared with the Penalized Least Square Criterion procedure on simulated data sets. Eventually, we apply Filtered Derivative with p-Value method to segmentation of heartbeat time series, and detection of change points in the average daily volume of financial time series

Fast change point analysis on the Hurst index of piecewise fractional Brownian motion

In this presentation, we introduce a new method for change point analysis on the Hurst index for a piecewise fractional Brownian motion. We first set the model and the statistical problem. The proposed method is a transposition of the FDpV (Filtered Derivative with p-value) method introduced for the detection of change points on the mean in Bertrand et al. (2011) to the case of changes on the Hurst index. The underlying statistics of the FDpV technology is a new statistic estimator for Hurst index, so-called Increment Bernoulli Statistic (IBS). Both FDpV and IBS are methods with linear time and memory complexity, with respect to the size of the series. Thus the resulting method for change point analysis on Hurst index reaches also a linear complexity

Second-Order Phase Transition Induced by Deterministic Fluctuations in Aperiodic Eight-State Potts Models

We investigate the influence of aperiodic modulations of the exchange interactions between nearest-neighbour rows on the phase transition of the two-dimensional eight-state Potts model. The systems are studied numerically through intensive Monte Carlo simulations using the Swendsen-Wang cluster algorithm for different aperiodic sequences. The transition point is located through duality relations, and the critical behaviour is investigated using FSS techniques at criticality. While the pure system exhibits a first-order transition, we show that the deterministic fluctuations resulting from the aperiodic coupling distribution are liable to modify drastically the physical properties in the neighbourhood of the transition point. For strong enough fluctuations of the sequence under consideration, a second-order phase transition is induced. The exponents $\beta/\nu$, $\gamma /\nu$ and $(1-\alpha)/\nu$ are obtained at the new fixed point and crossover effects are discussed. Surface properties are also studied.Comment: LaTeX file with EPJB macro package, 11 pages, 16 postscript figures, to appear in Eur. Phys. J.