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For a linear simultaneous equation model in
econometrics, one authoritative definition of identification is
Fisher's admissible transformation view. It takes parameter
restriction and covariance restriction together into consideration.
We show that the covariance restriction on disturbance terms may
obstruct the exclusion restriction on variables. The exclusion
restriction on variables is necessary for the test of
observationally equivalent of equations so that the union
identification does not hold true.
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In this paper, we shall study
how Revuz measures, energy functional, capacity and Lvy system
change under Girsanov transform of Hunt processes.
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In this paper, strong limit theorem of Markov
chains is discussed by the martingale difference convergence theorem
in the random environments, by which the strong laws of large number
of Markov chains are got. Some laws we got broaden the using area of
some results already have.
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This article studies the indifference prices
and hedging strategies in the presence of proportional transaction
costs in the incomplete markets. By introducing a new probabilistic
measure, the indifference price and the corresponding hedging
strategy can be got in one period model. A probabilistic iterative
algorithm is constructed for indifference prices of claims in a
multiperiod incomplete model. And the corresponding pricing measure
and the hedging strategy can be got. Pricing measure proves a
martingale measure if the transaction cost is zero.
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The study of empirical risk minimization
(ERM) algorithm associated with least squared loss is one of very
important issues in statistical learning theory. The main results
describing the learning rates of ERM regression are almost based on
independent and identically distributed (i.i.d.) inputs. However,
independence is a very restrictive concept. In this paper we go far
beyond this classical framework by establishing the bound on the
learning rates of ERM regression with geometrically -mixing
inputs. We prove that the ERM regression with geometrically
-mixing inputs is consistent and the main results obtained in
this paper are also suited to a large class of Markov chains samples
and hidden Markov models.
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Cox model is the most popular model in
modelling the relationship between a survival outcome and predictive
covariates, and has been gotten great success about regression
modelling survival data. It is well known that the maximum partial
likelihood estimates of regression parameters of Cox model are
consistent, asymptotically normal and semiparametrically efficient.
In this paper, based on marginal proportional hazards model and a
partitioning-based method, we develop an approach to improve
estimation efficiency of regression parameters in Cox model through
introducing some other handy or easily collected survival data.
Practically, for each subject, there frequently exist some other
possibly-multivariate survival data available in addition to the
main endpoint of survival times, which are easily collected or
handy, and belong to the same subject or group as the survival data
of interest. All the data construct multivariate survival data, and
the famous WLW model, one important marginal proportional hazards
model of multivariate survival data proposed in 1989 by Wei, Lin and
Weissfels (1989), is the model of natural choice to regression
modelling the aforementioned multivariate survival data. But, as
pointed out in this paper, by making direct use of the WLW method ,
the estimation efficiency of regression parameters of interest
cannot be improved. Based on the partitioning-based method for WLW
model, an approach to improve the estimation of regression parameter
of Cox model is proposed and discussed. Simulation studies are
conducted to investigate behavior of the proposed approach under
practical sample size. Our results show that it performs well, only
if the constructed multivariate survival data are correlated between
the survival data of interest and the introduced survival data, even
for small to moderate sample size.
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In this paper we discuss relation between the
Martin entrance boundary and Ray-Knight compactification of with
respect to a honest minimal Q-processes, and mainly obtain the
bijective mapping between the Martin entrance boundary of minimal
Q-processes and in Ray-Knight compactification
when is finite.
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This paper considers the state space representation
for the ARFIMA-GARCH model, which combines both the long memory time
series and the conditional heteroscedastic processes. Although this
state space representation is infinite dimensional, an exact maximum
likelihood (ML) estimator based on this kind of representation can
be computed in a finite number of iterations. Quasi ML estimators
based on the autoregressive approximation for the likelihood
function are proposed. Due to the facility of the state space
representation, these estimation approaches can be easily applied to
the missing data case. Simulation results of both the non-missing
data case and the missing data case are reported. A real data
example from stock market illustrates the proposed method.
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In this paper, we discuss about the high
degree stochastic order of real-valued random variables. We generate
new and elegant characters for the th stop-loss order over the
real-valued risks, based on which the th order both on survival
function and on distribution function are achieved. Furthermore, we
study three high degree economic orders that based on different
utility functions, the relations and properties of these orders are
obtained.
