On the structure of stationary flat processes

The paper contains some results related to the fundamental question of Davidson whether all rotationally stationary line processes in the plane which have a.s. no parallel lines are Cox (i.e. doubly stochastic Poisson processes). This problem is shown to be equivalent to the corresponding one for stationarity under translations only. The partial solutions by Papangelou are improved in various directions, and they are further extended to the case of marked k-dimensional flats (hyperplanes) in R ^d for arbitrary k and d with 0<k<d. It turns out that the main result of Papangelou carries over to the case k d/2, while the opposite case seems to require stronger regularity assumptions. In the former case, stationarity is typically needed in 2(d–k) directions only. The present treatment (like the one of Papangelou) proceeds in two steps, in proving first that sufficiently smooth stationary random measures are invariant, and second that point processes without parallel atoms and with invariant conditional intensities are Cox. In the final section, some related problems are discussed which provide some further insight into the structure of the basic Davidson problem (which remains open).     

On the structure of stationary flat processes. II

Summary The present paper continues the work by Davidson, Krickeberg, Papangelou, and the author on proving, under weakest possible assumptions, that a stationary random measure or a simple point process on the space of k-flats in R ^d is a.s. invariant or a Cox process respectively. The problems for and are related by the fact that is Cox whenever the Papangelou conditional intensity measure of (a thinning of) is a.s. invariant. In particular, is shown to be a.s. invariant, whenever it is absolutely continuous with respect to some fixed measure and has no (so called) outer degeneracies. When k=d–22, no absolute continuity is needed, provided that the first moments exist and that has no inner degeneracies either. Under a certain regularity condition on , it is further shown that and are simultaneously non-degenerate in either sense.     

On the dynamics of stationary shift processes with Cantor structure

Considering the stationary processes generated by shift transformations on selfsimilar sets, we study the transformations of the spectral density of the sets and establish the law of energy exchange. The energy transfer is modeled by interaction of the quasiparticles representing the processes in hyperbolic geometry.     

Isoperimetric problems and roses of neighborhood for stationary flat processes

The paper yields necessary conditions for the directional distributions of stationary k–flat processes in ?^d that maximize their intersection density of order 2, that is, the mean (2k – d)–volume of their self–intersections in an observation window of unit d–volume. The conditions are given in terms of the rose of intersections (i.e., the intensity of intersections of the flat process with test flats). The notion of the rose of neighborhood is introduced which is an analogue of the rose of intersections for lower dimensional flat processes. Some properties of the rose of neighborhood are studied and an asymptotically unbiased estimator is given.     

On the structure of stationary sets

We isolate several classes of stationary sets of [k]ωand investigate implications among them. Under a large cardinal assumption, we prove a structure theorem for stationary sets.     

On the stationary measures of critical branching processes

Let Z _n (n=0, l, ...) be an aperiodic critical Galton-Watson process and let ² be the (possibly infinite) variance of Z ₁. Let _k (k=1, 2, ...) denote the stationary measure of the process. Kesten, Ney and Spritzer proved in 1966 that _k 2/ ² as k (*) under the additional assumption that EZ ₁ ² log Z ₁< (**) In the present paper, (*) is proved without the assumption (**). The proof uses complex function theory.     

On the convergence of sequences of stationary jump Markov processes

This paper presents two main results: first, a Liapunov type criterion for the existence of a stationary probability distribution for a jump Markov process; second, a Liapunov type criterion for existence and tightness of stationary probability distributions for a sequence of jump Markov processes. If the corresponding semigroups T_N(t) converge, under suitable hypotheses on the limit semigroup, this last result yields the weak convergence of the sequence of stationary processes (T_N(t), π_N) to the stationary limit one.     

On a class of asymptotically stationary harmonizable processes

We prove that every harmonizable process with σ-finite bimeasure is asymptotically stationary and we give its associated spectral measure.     

On the differentiability of a class of stationary gaussian processes

For a stationary Gaussian process either almost all sample paths are almost everywhere differentiable or almost all sample paths are almost nowhere differentiable. In this paper it is shown by means of an example involving a random lacunary trigonometric series that “almost everywhere differentiable” and “almost nowhere differentiable” cannot in general be replaced by “everywhere differentiable” and “nowhere differentiable”, respectively.     

On marginal distributions and isomorphisms of stationary processes

LetT be an invertible ergodic aperiodic measure preserving transformation of a Lebesgue space, letA be a finite alphabet, and let π be a probability measure onA ⁿ which admits a mixing shift-invariant measureμ _π onΩ=A ^? such that the marginals of anyn successive coordinates are π and the entropyh(T) ofT is smaller than the entropy of the shift in (Ω,μ _π). Then there exists a shift invariant measure ν_π in Ω which also has marginals π and for whichT is isomorphic to the shift in (Ω, ν_π). This contains Krieger's finite generator theorem and strengthens the measure theoretic part of his approximation theorem for shift-invariant measures by showing that the preassigned marginal π can not only be achieved up to an ε>0 but exactly. Our result also contains an as yet unpublished theorem of Krieger, which says thatT can be embedded in an arbitrary mixing subshift of finite type, as long as the entropy of the subshift under the measure with maximal entropy exceeds that ofT. In the final section we show that the method can be extended to yield also exact marginals for the generator in the Jewett-Krieger theorem, i.e.T is shown to be isomorphic to a shift in (Ω, ν_π) where ν_π has exact marginals π and the shift is uniquely ergodic on the support of ν_π.     

On the convergence of finite linear predictors of stationary processes

It is shown that the finite linear least-squares predictor of a multivariate stationary process converges to its Kolmogorov-Wiener predictor at an exponential rate, provided that the entries of its spectral density matrix are smooth functions. Also, the same rate of convergence holds for the partial sums of the Kolmogorov-Wiener predictor.     

On the structure of completely flat Banach spaces

Completely flat Banach spaces (i.e. Banach spaces having a spanning centrally symmetric closed curve in the unit sphere with length 4), are characterized as spaces ‘between’ the two classical examples L₁[0,1] and C_±[0,1]. Some applications are given.     

On the Bayesian estimation for the stationary Neyman-Scott point processes

The pure and modified Bayesian methods are applied to the estimation of parameters of the Neyman-Scott point process. Their performance is compared to the fast, simulation-free methods via extensive simulation study. Our modified Bayesian method is found to be on average 2.8 times more accurate than the fast methods in the relative mean square errors of the point estimates, where the average is taken over all studied cases. The pure Bayesian method is found to be approximately as good as the fast methods. These methods are computationally affordable today.     

On the construction of Wold decomposition for multivariate stationary processes

A method to construct the Wold decomposition for multivariate stationary stochastic processes x_k, k Z, is presented. The method is based on orthogonal decompositions for x_k, k Z, obtained by forming orthogonal projections of x_k, k Z, onto its component processes , k Z, j = 1, …, q. The method does not give a complete solution to the Wold decomposition problem.     

On stationary Markov processes with polynomial conditional moments

We study a class of stationary Markov processes with marginal distributions identifiable by moments such that every conditional moment of degree say m is a polynomial of degree at most m. We show that then under some additional, natural technical assumption there exists a family of orthogonal polynomial martingales. More precisely we show that such a family of processes is completely characterized by the sequence {(α_n, p_n)}_{n ? 0} where α′_ns are some positive reals while p′_ns are some monic orthogonal polynomials. Bakry and Mazet (Séminaire de Probabilit?s, vol. 37, 2003) showed that under some additional mild technical conditions each such sequence generates some stationary Markov process with polynomial regression.

We single out two important subclasses of the considered class of Markov processes. The class of harnesses that we characterize completely. The second one constitutes of the processes that have independent regression property and are stationary. Processes with independent regression property so to say generalize ordinary Ornstein–Uhlenbeck (OU) processes or can also be understood as time scale transformations of Lévy processes. We list several properties of these processes. In particular we show that if these process are time scale transforms of Lévy processes then they are not stationary unless we deal with classical OU process. Conversely, time scale transformations of stationary processes with independent regression property are not Lévy unless we deal with classical OU process.     

On smoothed probability density estimation for stationary processes

Aspects of estimation of the (marginal) probability density for a stationary sequence or continuous parameter process, are considered in this paper. Consistency and asymptotic distributional results are obtained using a class of smoothed function estimators including those of kernel type, under various decay of dependence conditions for the process. Some of the consistency results contain convergence rates which appear to be more delicate than those previously available, even for i.i.d. sequences.