4,439 research outputs found
On a discrete Hill's statistical process based on sum-product statistics and its finite-dimensional asymptotic theory
The following class of sum-product statistics
T_n(p)=\frac{1}{k}\sum_{h=1}^p \sum_{(s_1...s_h)\in P(p,h)}
\sum_{i_1=l+1}^{i_0} ... \sum_{i_h=l+1}^{i_{h-1}} i_h \prod_{i=i_1}^{i_h}
\frac{(Y_{n-i+1,n}-Y_{n-i,n})^{s_i}}{s_i!}
(where and n are positive integers, is the
set of all ordered parititions of into positive integers and
are the order statistics based on a sequence of
independent random variables with underlying distribution
), is introduced. For each p,
is an estimator of the index of a distribution whose upper
tail varies regularly at infinity. \ This family generalizes the so called Hill
statistic and the Dekkers-Einmahl-De Haan one. We study the limiting laws of
the process and completely describe the covariance
function of the Gaussian limiting process with the help of combinatorial
techniques. Many results available for Hill's statistic regarding asymptotic
normality and laws of the iterated logarithm are extended to each margin
, for fixed, and for any distribution function lying in the
extremal domain. In the process, we obtain special classes of numbers related
to those of paths joining the opposite coins within a parallelogram.Comment: 22 page
Strong limits related to the oscillation modulus of the empirical process based on the k-spacing process
Recently, several strong limit theorems for the oscillation moduli of the
empirical process have been given in the iid-case. We show that, with very
slight differences, those strong results are also obtained for some
representation of the reduced empirical process based on the (non-overlapping)
k-spacings generated by a sequence of independent random variables (rv's)
uniformly distributed on . This yields weak limits for the mentioned
process. Our study includes the case where the step k is unbounded. The results
are mainly derived from several properties concerning the increments of gamma
functions with parameters k and one.Comment: 23 page
A simple proof of the theorem of Sklar and its extension to distribution functions
In this note we provide a quick proof of the Sklar's Theorem on the existence
of copulas by using the generalized inverse functions as in the one dimensional
case, but a little more sophisticated.Comment:
How to use the functional empirical process for deriving asymptotic laws for functions of the sample
The functional empirical process is a very powerful tool for deriving
asymptotic laws for almost any kind of statistics whenever we know how to
express them into functions of the sample. Since this method seems to be
applied more and more in the very recent future, this paper is intended to
provide a complete but short description and justification of the method and to
illustrate it with a non-trivial example using bivariate data. It may also
serve for citation whithout repeating the arguments.Comment: 11 page
The weak limiting behavior of the de Haan-Resnick estimator of the exponent of a stable distribution
The problem of estimating the exponent of a stable law received a
considerable attention in the recent literature. Here, we deal with an estimate
of such a exponent introduced by De Haan and Resnick when the corresponding
distribution function belongs to the Gumbel's domain of attraction. This study
permits to construct new statistical tests. Examples and simulations are given.
The limiting law are shown to be the Gumbel's law and particular cases are
given with norming constants expressed with iterated logarithms and
exponentials.Comment: 17 page
A remark on the asymptotic tightness in
In this note, we extend a simple criteria for uniform tightness in ,
the class of real continuous functions defined on , given in Theorem 8.3
of Billingsley to the asymptotic tightness in , the
class of real bounded functions defined on with , in the lines of
Theorems 1.5.6 and 1.5.7 in van der vaart and Wellner.Comment: 4 page
A note on the asymptotic normality of sums of extreme values
Let , ,... be a sequence of independent random variables with
common distribution function in the domain of attraction of a Gumbel
extreme value distribution and for each integer , let denote the order statistics based on the first of these random
variables. Along with related results it is shown that for any sequence of
positive integers and as the sum of the upper extreme values
, when properly centered and normalized, converges
in distribution to a standard normal random variable . These results
constitute an extension of results by S. Cs\"{o}rg\H{o} and D.M. Mason (1985).Comment: 1
A simple note on some empirical stochastic process as a tool in uniform L-statistics weak laws
In this paper, we are concerned with the stochastic process \begin{equation}
\beta_{n}(q_{t},t)=\beta_{n}(t)=\frac{1}{\sqrt{n}}\sum_{j=1}^{n}\left\{G_{t,n}(Y(t))-G_{t}(Y_{j}(t))\right\}
q_{t}(Y_{j}(t)), \tag{A} \end{equation} where for and , the
sequences are independant
observations of some real stochastic process , for each , is the distribution function of and is
the empirical distribution function based on , and finally is a bounded real fonction
defined on . This process appears when investigating some
time-dependent L-Statistics which are expressed as a function of some
functional empirical process and the process (A). Since the functional
empirical process is widely investigated in the literature, the process reveals
itself as an important key for L-Statistics laws. In this paper, we state an
extended study of this process, give complete calculations of the first
moments, the covariance function and find conditions for asymptotic tightness.Comment: 11 pag
The General Poverty Index
We introduce the General Poverty Index (GPI), which summarizes most of the
known and available poverty indices, in the form {equation*} GPI=\delta
(\frac{A(Q_{n},n,Z)}{nB(Q,n)}\overset{Q_{n}}{\underset{j=1}{\sum}%}w(\mu_{1}n+\mu_{2}Q_{n}-\mu_{3}j+\mu_{4})d(\frac{Z-Y_{j,n}}{Z}%)),{equation*}
where {equation*} B(Q_{n},n)=\sum_{j=1}^{Q}w(j), {equation*}
\ are given measurable functions, is the
number of the poor in the sample, Z is the poverty line and \ are the ordered sampled incomes or expenditures
of the individuals or households. We show here how the available indices based
on the poverty gaps are derived from it. The asymptotic normality is then
established and particularized for the usual poverty measures for immediate
applications to poor countries data.Comment: 17 page
VB and R codes using Households databases available in the NSI's : A prelude to statistical applied studies
We describe the main features of the households databases we can find in most
of our National Statistics Institute. We provide algorithms aimed at extracting
a diversity of variables on which different statistical procedures may be
applied. Here, we particularly focus on the scaled income, as a beginning.
Associated codes (MS Visual Basic and R codes) have been successfully tested
and delivered in the text and in a separate fileComment: 42 pages, 3 figure
- β¦