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Search Results: 1 - 10 of 109 matches for " Phang Piau "
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Simple Procedure for the Designation of Haar Wavelet Matrices for Differential Equations
Phang Chang,Phang Piau
Lecture Notes in Engineering and Computer Science , 2008,
Abstract:
Haar Wavelet Matrices Designation in Numerical Solution of Ordinary Differential Equations
Phang Chang,Phang Piau
IAENG International Journal of Applied Mathematics , 2008,
Abstract:
Asymptotics of iterated branching processes
Didier Piau
Mathematics , 2005,
Abstract: We study the iterated Galton-Watson process (IGW), possibly with thinning, introduced by Gawe{\l}and Kimmel to model the number of repeats of DNA triplets during some genetic disorders. If the process involves some thinning, then extinction and explosion can have positive probability simultaneously. If the underlying (simple) Galton-Watson process is nondecreasing with mean m, then, conditionally on the explosion, the logarithm of the population of the IGW at time n+1 is equivalent to log(m) times the population at time n, almost surely. This simplifies arguments of Gawe{\l}and Kimmel, and confirms and extends a conjecture of Pakes.
Maximal generalization of Baum-Katz theorem and optimality of sequential tests
Didier Piau
Mathematics , 2005,
Abstract: Baum-Katz theorem asserts that the Cesaro means of i.i.d. increments distributed like X r-converge if and only if |X|^{r+1} is integrable. We generalize this, and we unify other results, by proving that the following equivalence holds, if and only if G is moderate: the Cesaro means G-converge if and only if G(L(a)) is integrable for every a if and only if |X|.G(|X|) is integrable. Here, L(a) is the last time when the deviation of the Cesaro mean from its limit exceeds a, and G-convergence is the analogue of r-convergence. This solves a question about the asymptotic optimality of Wald's sequential tests.
Invariance principle for the coverage rate of genomic physical mappings
Didier Piau
Mathematics , 2005,
Abstract: We study some stochastic models of physical mapping of genomic sequences. Our starting point is a global construction of the process of the clones and of the process of the anchors which are used to map the sequence. This yields explicit formulas for the moments of the proportion occupied by the anchored clones, even in inhomogeneous models. This also allows to compare, in this respect, inhomogeneous models to homogeneous ones. Finally, for homogeneous models, we provide nonasymptotic bounds of the variance and we prove functional invariance results.
On two duality properties of random walks in random environment on the integer line
Didier Piau
Mathematics , 2005,
Abstract: According to Comets, Gantert and Zeitouni on the one hand and to Derriennic on the other hand, some functionals associated to the hitting times of random walks in random environment on the integer line coincide, for the walk itself and for the walk in the reversed environment. We show that these two duality principles are algebraically equivalent, that they both stem from the Markov property of the walk in a fixed environment, and not of the ergodicity of the model, and that there exists finitist and almost sure versions of this duality.
Harmonic moments of non homogeneous branching processes
Didier Piau
Mathematics , 2005,
Abstract: We study the harmonic moments of Galton-Watson processes, possibly non homogeneous, with positive values. Good estimates of these are needed to compute unbiased estimators for non canonical branching Markov processes, which occur, for instance, in the modeling of the polymerase chain reaction. By convexity, the ratio of the harmonic mean to the mean is at most 1. We prove that, for every square integrable branching mechanisms, this ratio lies between 1-A/k and 1-B/k for every initial population of size k greater than A. The positive constants A and B, such that B is at most A, are explicit and depend only on the generation-by-generation branching mechanisms. In particular, we do not use the distribution of the limit of the classical martingale associated to the Galton-Watson process. Thus, emphasis is put on non asymptotic bounds and on the dependence of the harmonic mean upon the size of the initial population. In the Bernoulli case, which is relevant for the modeling of the polymerase chain reaction, we prove essentially optimal bounds that are valid for every initial population. Finally, in the general case and for large enough initial populations, similar techniques yield sharp estimates of the harmonic moments of higher degrees.
Harmonic continuous-time branching moments
Didier Piau
Mathematics , 2005, DOI: 10.1214/105051606000000493
Abstract: We show that the mean inverse populations of nondecreasing, square integrable, continuous-time branching processes decrease to zero like the inverse of their mean population if and only if the initial population $k$ is greater than a first threshold $m_1\ge1$. If, furthermore, $k$ is greater than a second threshold $m_2\ge m_1$, the normalized mean inverse population is at most $1/(k-m_2)$. We express $m_1$ and $m_2$ as explicit functionals of the reproducing distribution, we discuss some analogues for discrete time branching processes and link these results to the behavior of random products involving i.i.d. nonnegative sums.
Asymptotics of posteriors for binary branching processes
Didier Piau
Mathematics , 2008,
Abstract: We compute the posterior distributions of the initial population and parameter of binary branching processes, in the limit of a large number of generations. We compare this Bayesian procedure with a more na\"ive one, based on hitting times of some random walks. In both cases, central limit theorems are available, with explicit variances.
Confidence intervals for nonhomogeneous branching processes and polymerase chain reactions
Didier Piau
Mathematics , 2005, DOI: 10.1214/009117904000000775
Abstract: We extend in two directions our previous results about the sampling and the empirical measures of immortal branching Markov processes. Direct applications to molecular biology are rigorous estimates of the mutation rates of polymerase chain reactions from uniform samples of the population after the reaction. First, we consider nonhomogeneous processes, which are more adapted to real reactions. Second, recalling that the first moment estimator is analytically known only in the infinite population limit, we provide rigorous confidence intervals for this estimator that are valid for any finite population. Our bounds are explicit, nonasymptotic and valid for a wide class of nonhomogeneous branching Markov processes that we describe in detail. In the setting of polymerase chain reactions, our results imply that enlarging the size of the sample becomes useless for surprisingly small sizes. Establishing confidence intervals requires precise estimates of the second moment of random samples. The proof of these estimates is more involved than the proofs that allowed us, in a previous paper, to deal with the first moment. On the other hand, our method uses various, seemingly new, monotonicity properties of the harmonic moments of sums of exchangeable random variables.
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