Tracking a random walk first-passage time through noisy observations

An optimization based framework for region wise optimal clusters in MR images using hybrid objective

A stopping time T is the first time when a trajectory of a stochastic process satisfies a specific criterion. In this paper, we use martingale theory to derive the integral fluctuation relation for the stochastic entropy production in a stationary physical system at stochastic stopping times T. This fluctuation relation implies the law , which states that it is not possible to reduce entropy on average, even by stopping a stochastic process at a stopping time, and which we call the second law of thermodynamics at stopping times. This law bounds the average amount of heat and work a system can extract from its environment when stopped at a random time. Furthermore, the integral fluctuation relation implies that certain fluctuations of entropy production are universal or are bounded by universal functions. These universal properties descend from the integral fluctuation relation by selecting appropriate stopping times: for example, when T is a first-passage time for entropy production, then we obtain a bound on the statistics of negative records of entropy production. We illustrate these results on simple models of nonequilibrium systems described by Langevin equations and reveal two interesting phenomena. First, we demonstrate that isothermal mesoscopic systems can extract on average heat from their environment when stopped at a cleverly chosen moment and that the second law at stopping times bounds the average extracted heat. Second, we demonstrate that the efficiency at stopping times of an autonomous stochastic heat engine, such as Feymann’s ratchet, can be larger than the Carnot efficiency and that the second law of thermodynamics at stopping times bounds its efficiency at stopping times.

The first-passage time, defined as the time a random walker takes to reach a target point in a confining domain, is a key quantity in the theory of stochastic processes. Its importance comes from its crucial role in quantifying the efficiency of processes as varied as diffusion-limited reactions, target search processes or the spread of diseases. Most methods of determining the properties of first-passage time in confined domains have been limited to Markovian (memoryless) processes. However, as soon as the random walker interacts with its environment, memory effects cannot be neglected: that is, the future motion of the random walker does not depend only on its current position, but also on its past trajectory. Examples of non-Markovian dynamics include single-file diffusion in narrow channels, or the motion of a tracer particle either attached to a polymeric chain or diffusing in simple or complex fluids such as nematics, dense soft colloids or viscoelastic solutions. Here we introduce an analytical approach to calculate, in the limit of a large confining volume, the mean first-passage time of a Gaussian non-Markovian random walker to a target. The non-Markovian features of the dynamics are encompassed by determining the statistical properties of the fictitious trajectory that the random walker would follow after the first-passage event takes place, which are shown to govern the first-passage time kinetics. This analysis is applicable to a broad range of stochastic processes, which may be correlated at long times. Our theoretical predictions are confirmed by numerical simulations for several examples of non-Markovian processes, including the case of fractional Brownian motion in one and higher dimensions. These results reveal, on the basis of Gaussian processes, the importance of memory effects in first-passage statistics of non-Markovian random walkers in confinement.

Least absolute deviation (LAD) is a well-known criterion to fit statistical models, but little is known about LAD estimation in structural equation modeling (SEM). To address this gap, the authors use the LAD criterion in SEM by minimizing the sum of the absolute deviations between the observed and the model-implied covariance matrices. Using Monte Carlo simulations, the authors compare the performance of this LAD estimator along several dimensions (bias, efficiency, convergence, frequencies of improper solutions, and absolute percentage deviation) to the full information maximum likelihood (ML) and unweighted least squares (ULS) estimators in structural equation modeling. The results for LAD are mixed: There are special conditions under which the LAD estimator outperforms ML and ULS, but the simulation evidence does not support a general claim that LAD is superior to ML and ULS in small samples.

The theory of random walks holds significance across various domains. To this end, this paper discusses the application of random walk theory in the modeling and analysis of complex systems, including the Brownian motion, Wiener process, and Pu migration. The first part mainly explains the concepts of Brownian motion and random walks, as well as their relationship. The core characteristic is the independence and normal distribution of increments, which makes it a special model of random walks. The next part is the Wiener process, which is a continuous time stochastic process. It can describe random phenomena and solve complex problems in finance, encompassing areas such as stock price prediction, option pricing, and risk management. The last part is about Pu migration, with the clear introduction of Pu migration in biology. The authors discuss why the random walk theory can be regarded as the better approach to analysis this problem. Thus, this paper highlights the importance of the random walk in solving many scientific issues.

Grey wolf optimizer (GWO) is a widely applied nature-inspired optimization algorithm with simple search mechanism, which suffers the deficiency of premature convergence. To improve its exploration performance and avoid trapping in to stagnation, we integrate the basic GWO with two random walk strategies, Gaussian random walk and Levy walk, and this new GWO extension is named RWGWO. The performance of RWGWO is compared with those of other comprising GWO variants by using several standard benchmarks. Moreover, RWGWO is applied in solving constrained engineering design problems. With a view to the experimental results, RWGWO has remarkable performance compared with the competitors in this work in terms of convergence speed and accuracy.

Compact representation of signals and images is a key for many applications. Compactness is often achieved through linear transforms with good energy concentration property. We present an adaptive wavelet filter bank with fixed number of vanishing moments, plus additional local adaptation. Proposed adaptation method is conducted at each sample according to the least absolute deviation (LAD) criterion. Fixed vanishing moments provide for polynomial annihilation. Adaptation is aimed to achieve maximum sparseness for a wider class of signals, such as sine waves. LAD criterion results in more accurate adaptation on sudden changes of signal statistics. In this paper, an efficient LAD realization is proposed, in spite of nonexistence of the closed form solution. Combining least squares and LAD criterion, we have achieved unbiased adaptation, robust to noise. Due to its simplicity and acceptable computational speed, the proposed scheme is a good candidate for the real-world applications. In this paper, advantages of the proposed scheme are shown in signal denoising and reconstruction.

In this Letter, we derive a relationship between the moments of the first-passage time for a random walk and the first-passage time density for subdiffusive processes modeled by continuous-time random walks. In particular, we show that the exact long-time behavior of the density depends only on the mean first-passage time of the corresponding normal diffusive process. In addition, we give explicit evaluations of the first-passage time distribution for general three-dimensional bounded domains. These results are relevant to systems involving anomalous diffusion in confinements.

We discuss a few principles to guide the design of efficient Metropolis–Hastings proposals for well-behaved target distributions without deeply divided modes. We illustrate them by developing and evaluating novel proposal kernels using a variety of target distributions. Here, efficiency is measured by the variance ratio relative to the independent sampler. The first principle is to introduce negative correlation in the MCMC sample or to reduce positive correlation: to propose something new, propose something different. This explains why single-moded proposals such as the Gaussian random-walk is poorer than the uniform random walk, which is in turn poorer than the bimodal proposals that avoid values very close to the current value. We evaluate three new bimodal proposals called Box, Airplane and StrawHat, and find that they have similar performance to the earlier Bactrian kernels, suggesting that the general shape of the proposal matters, but not the specific distributional form. We propose the “Mirror” kernel, which generates new values around the mirror image of the current value on the other side of the target distribution (effectively the “opposite” of the current value). This introduces negative correlations, leading in many cases to efficiency of >100%>. The second principle, applicable to multidimensional targets, is that a sequence of well-designed one-dimensional proposals can be more efficient than a single d-dimensional proposal. Thirdly, we suggest that variable transformation be explored as a general strategy for designing efficient MCMC kernels. We apply these principles to a high-dimensional Gaussian target with strong correlations, a logistic regression problem and a molecular clock dating problem to illustrate their practical utility.

Fractal measures of first passage time of a simple random walk

In order to identify the parameters of nonlinear Hammerstein model which are contaminated by colored noise and peak noise, the least absolute deviation (LAD) is selected as the objective function to solve the problem of large residual square when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable (SαS) distribution. However, LAD cannot meet the need of differentiability required by most algorithms. To improve robustness and to solve the nondifferentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function to replace absolute value under certain situations. The proposed method is derived from ALAD criterion and extended stochastic gradient method. Due to the differentiability of the objective function, we can get a recursive identification algorithm which is simple and easy to calculate compared with LAD. The convergence of the proposed identification method is also proved by Lyapunov stability theory, and the simulation experiments show that the proposed method has higher accuracy and stronger robustness than the least square (LS) method in the identification of Hammerstein model with colored noise and impulse noise. The impact of impulse noise can be restrained effectively.

The entropy rates of Markov chains (random walks) defined on connected undirected graphs are well studied in many surveys. We study the entropy rates related to the first-passage time probability distributions of fair random walks, their relative (Kullback–Leibler) entropies, and the entropy related to two biased random walks – with the random absorption of walkers and the shortest paths random walks. We show that uncertainty of first-passage times quantified by the entropy rates characterizes the connectedness of the graph. The relative entropy derived for the biased random walks estimates the level of uncertainty between connectivity and connectedness – the local and global properties of nodes in the graph.

Common econometric estimators such as least squares, least absolute deviations (LAD), instrumental variables, maximum likelihood, and semiparametric estimators are non-robust against data contamination. Despite the known superiority of high-breakdown point (HBP) estimators in such situations, the HBP estimators have rarely been used in economics. This article presents some applications of an HBP estimator called the S-estimator (Rousseeuw and Yohai, Robust and Nonlinear Time Series Analysis (Eds) W.H. Franke and R.D. Martin, Springer-Verlag, NY, pp. 256–72, 1984) to the estimation of a linear regression model and compares the results with those obtained by ordinary least squares (OLS) and LAD methods. It is found that significance of variables as well as signs of coefficient estimates can be quite different under HBP estimation than under OLS and LAD estimation.

Observations at the first opposition are used to determine the orbits of 16 near-Earth asteroids with two or more observed oppositions. The orbits are improved by the differential method. This paper considers two modifications of the improvement technique, which are compared to the classical method based on the principle of the least square method (LSM). The first modification uses the principle of least absolute deviations (LAD). In the second modification, the differences O - C (between the observed and calculated positions) are transformed to fit into a new coordinate system whereby the axes go parallel and perpendicular to the asteroid's apparent path (the modified differential method (MDM)). The orbits determined from one opposition by the classical LSM, LAD, and MDM are compared to a more accurate orbit calculated by the LSM from all the available oppositions. The calculations show that in 13 cases out of 16, the asteroid orbits calculated by LAD are more accurate than those calculated by the classical LSM. The improvement by the modified differential method, which includes the O - C transformation, does not produce any perceptible increase in accuracy when compared to the orbits calculated by the classical method.

In general linear modeling, an alternative to the method of least squares (LS) is the least absolute deviations (LAD) procedure. Although LS is more widely used, the LAD approach yields better estimates in the presence of outliers. In this paper, we examine the performance of LAD estimators for the parameters of the first‐order autoregressive model in the presence of outliers. A simulation study compared these estimates with those given by LS. The general conclusion is that LAD does not deal successfully with additive outliers. A simple procedure is proposed which allows exception reporting when outliers occur.