Iterative Methods for Overflow Queuing Models II.
Kermack-McKendrick epidemics vaccinated
This paper proposes a deterministic model for the spread of an epidemic. We extend the classical Kermack–McKendrick model, so that a more general contact rate is chosen and a vaccination added. The model is governed by a differential equation (DE) for the time dynamics of the susceptibles, infectives and removals subpopulation. We present some conditions on the existence and uniqueness of a solution to the nonlinear DE. The existence of limits and uniqueness of maximum of infected individuals are...
Kloosterman-type sums and the discrepancy of nonoverlapping pairs of inversive congruential pseudorandom numbers
Knowledge-based Green's kernel for support vector regression.
Kolmogorov complexity, pseudorandom generators and statistical models testing
An attempt to formalize heuristic concepts like strings (sequences resp.) “typical” for a probability measure is stated in the paper. Both generating and testing of such strings is considered. Kolmogorov complexity theory is used as a tool. Classes of strings “typical” for a given probability measure are introduced. It is shown that no pseudorandom generator can produce long strings from the classes. The time complexity of pseudorandom generators with oracles capable to recognize “typical” strings...
- and -discrepancy of (order 2) digital nets
Dick proved that all dyadic order 2 digital nets satisfy optimal upper bounds on the -discrepancy. We prove this for arbitrary prime base b with an alternative technique using Haar bases. Furthermore, we prove that all digital nets satisfy optimal upper bounds on the discrepancy function in Besov spaces with dominating mixed smoothness for a certain parameter range, and enlarge that range for order 2 digital nets. The discrepancy function in Triebel-Lizorkin and Sobolev spaces with dominating mixed...
L'algorithme SEM : un algorithme d'apprentissage probabiliste pour la reconnaissance de mélange de densités
Land valuation using a real option approach.
Large sample behaviour of the -transformation of two-sample rank statistics
Les processus de surveillance. Étude et simulation d'un cas
Likelihood ratio rank tests for the two-sample problem with randomly censored data
Linear comparative calibration with correlated measurements
The paper deals with the linear comparative calibration problem, i. e. the situation when both variables are subject to errors. Considered is a quite general model which allows to include possibly correlated data (measurements). From statistical point of view the model could be represented by the linear errors-in-variables (EIV) model. We suggest an iterative algorithm for estimation the parameters of the analysis function (inverse of the calibration line) and we solve the problem of deriving the...
Linear discriminant analysis with a generalization of the Moore-Penrose pseudoinverse
The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this paper, we propose improving LDA in this...
Linear-implicit strong schemes for Itô-Galerkin approximations of stochastic PDEs.
Linearization regions for confidence ellipsoids
If an observation vector in a nonlinear regression model is normally distributed, then an algorithm for a determination of the exact -confidence region for the parameter of the mean value of the observation vector is well known. However its numerical realization is tedious and therefore it is of some interest to find some condition which enables us to construct this region in a simpler way.
L'inverse d'une matrice d'intervalles
Lissage d'un fractile d'une distribution de probabilité variable
Local degeneracy of Markov chain Monte Carlo methods
We study asymptotic behavior of Markov chain Monte Carlo (MCMC) procedures. Sometimes the performances of MCMC procedures are poor and there are great importance for the study of such behavior. In this paper we call degeneracy for a particular type of poor performances. We show some equivalent conditions for degeneracy. As an application, we consider the cumulative probit model. It is well known that the natural data augmentation (DA) procedure does not work well for this model and the so-called...
Log-periodogram regression in asymmetric long memory
The long memory property of a time series has long been studied and several estimates of the memory or persistence parameter at zero frequency, where the spectral density function is symmetric, are now available. Perhaps the most popular is the log periodogram regression introduced by Geweke and Porter–Hudak [gewe]. In this paper we analyse the asymptotic properties of this estimate in the seasonal or cyclical long memory case allowing for asymmetric spectral poles or zeros. Consistency and asymptotic...
Loss protection in pairs trading through minimum profit bounds: A cointegration approach.