246 research outputs found
‘Decolonisation’ in China, 1949-1959
No full textIn this chapter Jonathan Howlett adopts perspectives and models from wider literatures on decolonisation to explore the Chinese Communist Party’s elimination of the British semi-colonial presence from China after the revolution of 1949 and to place it within its global context. He focuses in particular on the CCP’s attempts to address the economic, cultural and human legacies of semi-colonialism within a comparative context. In so doing, the author seeks to complicate our understanding of the Sino-British relationship by exploring one of its most dramatic phases and to further illuminate this neglected period in Chinese history
Necessary and sufficient conditions for the inversion of linearly-perturbed bounded linear operators on Banach space using Laurent series
No full textAmie R. Albrecht, Phil G. Howlett, Charles E.M. Pearc
The general solution to an autoregressive law of motion
No full textIn this article we provide a complete description of the set of all solutions
to an autoregressive law of motion in a finite-dimensional complex vector space.
Every solution is shown to be the sum of three parts, each corresponding to a
directed flow of time. One part flows forward from the arbitrarily distant past;
one flows backward from the arbitrarily distant future; and one flows outward
from time zero. The three parts are obtained by applying three complementary
spectral projections to the solution, these corresponding to a separation of the
eigenvalues of the autoregressive operator according to whether they are inside,
outside or on the unit circle. We provide a finite-dimensional parametrization of
the set of all solutions
The fundamental equations for inversion of operator pencils on Banach space
No full textAbstract not availableAmie Albrecht, Phil Howlett, Charles Pearc
Optimal estimation of a random signal from partially missed data
No full textWe provide a new technique for random signal estimation
under the constraints that the data is corrupted by random
noise and moreover, some data may be missed. We utilize
nonlinear filters defined by multi-linear operators of degree r,
the choice of which allows a trade–off between the accuracy
of the optimal filter and the complexity of the corresponding
calculations. A rigorous error analysis is presented.Anatoli Torokhti, Phil Howlett and Charles Pearc
The GJRT for auto-regressive time series on Banach space
No full textWe prove a generalized Granger–Johansen representation theorem (GJRT)
for finite or infinite order integrated auto-regressive time series on Banach space
The Granger-Johansen representation theorem for integrated time series on Banach space
No full textWe prove an extended Granger–Johansen representation theorem (GJRT) for finite or infinite order integrated autoregressive time series on Banach space. We assume only that the resolvent of the autoregressive polynomial for the series is analytic on and inside the unit circle except for an isolated singularity at unity. If the singularity is a pole of finite order the time series is integrated of the same order. If the singularity is an essential singularity the time series is integrated of order infinity. When there is no deterministic forcing the value of the
series at each time is the sum of an almost surely convergent stochastic trend, a deterministic term depending on the initial conditions and a finite sum of embedded white noise terms in the prior observations. This is the extended GJRT. In each case the original series is the sum
of two separate autoregressive time series on complementary subspaces - a singular component which is integrated of the same order as the original series and a regular component which is not integrated. The extended GJRT applies to all integrated autoregressive processes irrespective of the spatial dimension, the number of stochastic trends and cointegrating relations in the system, and the order of integration
Optimal recursive estimation of raw data
No full textThe original publication is available at www.springerlink.comWe present a new approach to the optimal estimation of random vectors. The approach is based on a combination of a specific iterative procedure and the solution of a best approximation problem with a polynomial approximant. We show that the combination of these new techniques allow us to build a computationally effective and flexible estimator. The strict justification of the proposed technique is provided.Anatoli Torokhti, Phil Howlett and Charles Pearc
Method of best hybrid approximations for constructing fixed rank optimal estimators
No full textP. Howlett, C. Pearce, A. Torokhtihttp://www.imub.ub.es/events/wc2004/prg.html
Method of best successive approximations for nonlinear operators
No full textThe original publication is available at www.springerlink.comWe present a new approach to the approximation of nonlinear operators in probability spaces. The approach is based on a combination of the specific iterative procedure and the best approximation problem solution with a quadratic approximant. We show that the combination of these new techniques allow us to build a computationally efficient and flexible method. The algorithm of the method and its application to the optimal filtering of stochastic signals are given.Anatoli Torokhti, Phil Howlett and Charles Pearc
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