2,297 research outputs found
First passage times of two-correlated processes: analytical results for the Wiener process and a numerical method for diffusion processes
Given a two-dimensional correlated diffusion process, we determine the joint
density of the first passage times of the process to some constant boundaries.
This quantity depends on the joint density of the first passage time of the
first crossing component and of the position of the second crossing component
before its crossing time. First we show that these densities are solutions of a
system of Volterra-Fredholm first kind integral equations. Then we propose a
numerical algorithm to solve it and we describe how to use the algorithm to
approximate the joint density of the first passage times. The convergence of
the method is theoretically proved for bivariate diffusion processes. We derive
explicit expressions for these and other quantities of interest in the case of
a bivariate Wiener process, correcting previous misprints appearing in the
literature. Finally we illustrate the application of the method through a set
of examples.Comment: 18 pages, 3 figure
Wind turbine condition monitoring strategy through multiway PCA and multivariate inference
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated
as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is
performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide
range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version
Robust isogeometric preconditioners for the Stokes system based on the Fast Diagonalization method
In this paper we propose a new class of preconditioners for the isogeometric
discretization of the Stokes system. Their application involves the solution of
a Sylvester-like equation, which can be done efficiently thanks to the Fast
Diagonalization method. These preconditioners are robust with respect to both
the spline degree and mesh size. By incorporating information on the geometry
parametrization and equation coefficients, we maintain efficiency on
non-trivial computational domains and for variable kinematic viscosity. In our
numerical tests we compare to a standard approach, showing that the overall
iterative solver based on our preconditioners is significantly faster.Comment: 31 pages, 4 figure
Discretization of Continuous Attributes
7 pagesIn the data mining field, many learning methods -like association rules, Bayesian networks, induction rules (Grzymala-Busse & Stefanowski, 2001)- can handle only discrete attributes. Therefore, before the machine learning process, it is necessary to re-encode each continuous attribute in a discrete attribute constituted by a set of intervals, for example the age attribute can be transformed in two discrete values representing two intervals: less than 18 (a minor) and 18 and more (of age). This process, known as discretization, is an essential task of the data preprocessing, not only because some learning methods do not handle continuous attributes, but also for other important reasons: the data transformed in a set of intervals are more cognitively relevant for a human interpretation (Liu, Hussain, Tan & Dash, 2002); the computation process goes faster with a reduced level of data, particularly when some attributes are suppressed from the representation space of the learning problem if it is impossible to find a relevant cut (Mittal & Cheong, 2002); the discretization can provide non-linear relations -e.g., the infants and the elderly people are more sensitive to illness
Computation of sum of squares polynomials from data points
We propose an iterative algorithm for the numerical computation of sums of
squares of polynomials approximating given data at prescribed interpolation
points. The method is based on the definition of a convex functional
arising from the dualization of a quadratic regression over the Cholesky
factors of the sum of squares decomposition. In order to justify the
construction, the domain of , the boundary of the domain and the behavior at
infinity are analyzed in details. When the data interpolate a positive
univariate polynomial, we show that in the context of the Lukacs sum of squares
representation, is coercive and strictly convex which yields a unique
critical point and a corresponding decomposition in sum of squares. For
multivariate polynomials which admit a decomposition in sum of squares and up
to a small perturbation of size , is always
coercive and so it minimum yields an approximate decomposition in sum of
squares. Various unconstrained descent algorithms are proposed to minimize .
Numerical examples are provided, for univariate and bivariate polynomials
Numerical Implementation of the QuEST Function
This paper deals with certain estimation problems involving the covariance
matrix in large dimensions. Due to the breakdown of finite-dimensional
asymptotic theory when the dimension is not negligible with respect to the
sample size, it is necessary to resort to an alternative framework known as
large-dimensional asymptotics. Recently, Ledoit and Wolf (2015) have proposed
an estimator of the eigenvalues of the population covariance matrix that is
consistent according to a mean-square criterion under large-dimensional
asymptotics. It requires numerical inversion of a multivariate nonrandom
function which they call the QuEST function. The present paper explains how to
numerically implement the QuEST function in practice through a series of six
successive steps. It also provides an algorithm to compute the Jacobian
analytically, which is necessary for numerical inversion by a nonlinear
optimizer. Monte Carlo simulations document the effectiveness of the code.Comment: 35 pages, 8 figure
Optimal rates and adaptation in the single-index model using aggregation
We want to recover the regression function in the single-index model. Using
an aggregation algorithm with local polynomial estimators, we answer in
particular to the second part of Question~2 from Stone (1982) on the optimal
convergence rate. The procedure constructed here has strong adaptation
properties: it adapts both to the smoothness of the link function and to the
unknown index. Moreover, the procedure locally adapts to the distribution of
the design. We propose new upper bounds for the local polynomial estimator
(which are results of independent interest) that allows a fairly general
design. The behavior of this algorithm is studied through numerical
simulations. In particular, we show empirically that it improves strongly over
empirical risk minimization.Comment: 36 page
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