On some identities for the Fibonomial coefficients via generating function

Some new identities for the Fibonomial coefficients are derived. These identities are related to the generating function of the kth powers of the Fibonacci numbers. Proofs are based on manipulation with the generating function of the sequence of “signed Fibonomial triangle”.     

On convergence of moment generating functions

Mukherjea et al. [Mukherjea, A., Rao, M., Suen, S., 2006. A note on moment generating functions. Statist. Probab. Lett. 76, 1185-1189] proved that if a sequence of moment generating functions M_n(t) converges pointwise to a moment generating function M(t) for all t in some open interval of the real line, not necessarily containing the origin, then the distribution functions F_n (corresponding to M_n) converge weakly to the distribution function F (corresponding to M). In this note, we improve this result and obtain conditions of the convergence which seem to be sharp: F_n converge weakly to F if M_n(t_k) converge to M(t_k), k=1,2,…, for some sequence {t₁,t₂,…} having the minimal and the maximal points. A similar result holds for characteristic functions.     

Multivariate normality via conditional specification

If X is a k-dimensional random vector, we denote by X_(i) the vector X with coordinate i deleted and by X_(i,j) the vector X with coordinates i and j deleted. If for each i the conditional distribution of X_i given X_(i) = x_(i) is univariate normal for each x_(i) ^K−1 and if for each i, j the conditional distribution of X_i given X_(i,j) = x_(i,j) is univariate normal for each x_(i,j) ^k−2 then it is shown that X has a classical k-variate normal distribution.     

On normality in function spaces

This work was supported by the Russian Foundation for Fundamental Research, Grant No. 93-011-1401.     

A generalization of the Curtiss theorem for moment generating functions

The Curtiss theorem deals with the relation between the weak convergence of probability measures on the line and the convergence of theirmoment generating functions in a neighborhood of zero. We present a multidimensional generalization of this result. To this end, we consider arbitrary σ-finite measures whose moment generating functions exist in a domain of multidimensional Euclidean space not necessarily containing zero. We also prove the corresponding converse statement.     

Enumeration of lattice paths and generating functions for skew plane partitions

n-dimensional lattice paths not touching the hyperplanesX _i–X _i+1=–1,i=1,2,...,n, are counted by four different statistics, one of which is MacMahon's major index. By a reflection-like proof, heavily relying on Zeilberger's (Discrete Math. 44(1983), 325–326) solution of then-candidate ballot problem, determinantal expressions are obtained. As corollaries the generating functions for skew plane partitions, column-strict skew plane partitions, reverse skew plane plane partitions and column-strict reverse skew plane partitions, respectively, are evaluated, thus establishing partly new results, partly new proofs for known theorems in the theory of plane partitions.     

A note on generating functions for Hausdorff moment sequences

For functions whose Taylor coefficients at the origin form a Hausdorff moment sequence we study the behaviour of for ( fixed).

     

Symplectic homology via generating functions

Research at Mathematical Sciences Research Institute supported by an MSRI postdoctoral fellowship. Research at Stanford supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship. Research at Centre Emile Borel, Institut Henri Poincaré, Paris funded by Ministère des Affaires Etrangères, France.     

Asymptotic normality of the likelihood moment estimators for a stationary linear process with heavy-tailed innovations

The authors recently proved in Martig and Hüsler (2016) that the likelihood moment estimators are consistent estimators for the parameters of the Generalized Pareto distribution for the case where the underlying data arises from a (stationary) linear process with heavy-tailed innovations. In this paper we derive the bivariate asymptotic normality under some additional assumptions and give an explicit example on how to check these conditions by using asymptotic expansions. Some finite sample comparisons are presented to investigate the bias and variance behavior for some of the estimators.     

Asymptotic behavior of empirical generating moment functions of random variables

Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 41, No. 7, pp. 992–994, July, 1989.     

Invariant Palm and related disintegrations via skew factorization

Let G be a measurable group with Haar measure ??, acting properly on a space S and measurably on a space T. Then any ??-finite, jointly invariant measure M on S?× T admits a disintegration ${\nu \otimes \mu}$ into an invariant measure ?? on S and an invariant kernel ?? from S to T. Here we construct ?? and??? by a general skew factorization, which extends an approach by Rother and Z?hle for homogeneous spaces S over G. This leads to easy extensions of some classical propositions for invariant disintegration, previously known in the homogeneous case. The results are applied to the Palm measures of jointly stationary pairs (??, ??), where ?? is a random measure on S and ?? is a random element in T.     

A bijective proof of the generating function for the number of reverse plane partitions via lattice paths

Recently, Hillman and Grassl gave a bijective proof for the generating function for the number of reverse plane partitions of a fixed shape λ. We give another bijective proof for this generating function via completelv different methods. Our bijection depends on a lattice path coding of reverse plane partitions and a new method for constructing bisections out of certain pairs of involutions due to Garsia and Milne.     

Testing multivariate normality in incomplete data of small sample size

In longitudinal studies with small samples and incomplete data, multivariate normal-based models continue to be a powerful tool for analysis. This has included a broad scope of biomedical studies. Testing the assumption of multivariate normality (MVN) is critical. Although many methods are available for testing normality in complete data with large samples, a few deal with the testing in small samples. For example, Liang et al. (J. Statist. Planning and Inference 86 (2000) 129) propose a projection procedure for testing MVN for complete-data with small samples where the sample sizes may be close to the dimension. To our knowledge, no statistical methods for testing MVN in incomplete data with small samples are yet available. This article develops a test procedure in such a setting using multiple imputations and the projection test. To utilize the incomplete data structure in multiple imputation, we adopt a noniterative inverse Bayes formulae (IBF) sampling procedure instead of the iterative Gibbs sampling to generate iid samples. Simulations are performed for both complete and incomplete data when the sample size is less than the dimension. The method is illustrated with a real study on an anticancer drug.     

A bijective proof of the generating function for the number of reverse plane partitions via lattice paths

Recently, Hillman and Grassl gave a bijective proof for the generating function for the number of reverse plane partitions of a fixed shape λ. We give another bijective proof for this generating function via completelv different methods. Our bijection depends on a lattice path coding of reverse plane partitions and a new method for constructing bisections out of certain pairs of involutions due to Garsia and Milne.     

Two tests for multivariate normality based on the characteristic function

We present two tests for multivariate normality. The presented tests are based on the Lévy characterization of the normal distribution and on the BHEP tests. The tests are affine invariant and consistent. We obtain the asymptotic null distribution of the test statistics using some results about generalized one-sample U-statistics, which are of independent interest.      

d-Orthogonality via generating functions

In this paper, starting from a suitable generating function of a polynomial set, we show how to decide whether the considered polynomial set is d-orthogonal and, if it is so, how to determine the corresponding d-dimensional functional vector. Then, we apply the obtained results to some known and new d-orthogonal polynomial sets. For the known ones, we give new proofs for some already obtained results.     

Invariant manifold theory via generating maps

We present a synthetic approach to invariant manifold theorems, based upon the notion of a generating map. To cite this article: M. Chaperon, C. R. Acad. Sci. Paris, Ser. I 346 (2008).