1,147 research outputs found
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting
This paper explores and develops alternative statistical representations and
estimation approaches for dynamic mortality models. The framework we adopt is
to reinterpret popular mortality models such as the Lee-Carter class of models
in a general state-space modelling methodology, which allows modelling,
estimation and forecasting of mortality under a unified framework. Furthermore,
we propose an alternative class of model identification constraints which is
more suited to statistical inference in filtering and parameter estimation
settings based on maximization of the marginalized likelihood or in Bayesian
inference. We then develop a novel class of Bayesian state-space models which
incorporate apriori beliefs about the mortality model characteristics as well
as for more flexible and appropriate assumptions relating to heteroscedasticity
that present in observed mortality data. We show that multiple period and
cohort effect can be cast under a state-space structure. To study long term
mortality dynamics, we introduce stochastic volatility to the period effect.
The estimation of the resulting stochastic volatility model of mortality is
performed using a recent class of Monte Carlo procedure specifically designed
for state and parameter estimation in Bayesian state-space models, known as the
class of particle Markov chain Monte Carlo methods. We illustrate the framework
we have developed using Danish male mortality data, and show that incorporating
heteroscedasticity and stochastic volatility markedly improves model fit
despite an increase of model complexity. Forecasting properties of the enhanced
models are examined with long term and short term calibration periods on the
reconstruction of life tables.Comment: 46 page
Unsupervised State-Space Modeling Using Reproducing Kernels
This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1109/TSP.2015.2448527.A novel framework for the design of state-space models (SSMs) is proposed whereby the state-transition function of the model is parametrised using reproducing kernels. The
nature of SSMs requires learning a latent function that resides
in the state space and for which input-output sample pairs are not
available, thus prohibiting the use of gradient-based supervised
kernel learning. To this end, we then propose to learn the mixing
weights of the kernel estimate by sampling from their posterior
density using Monte Carlo methods. We first introduce an offline
version of the proposed algorithm, followed by an online version
which performs inference on both the parameters and the hidden
state through particle filtering. The accuracy of the estimation
of the state-transition function is first validated on synthetic
data. Next, we show that the proposed algorithm outperforms
kernel adaptive filters in the prediction of real-world time series,
while also providing probabilistic estimates, a key advantage over
standard methods.Felipe Tobar acknowledges financial support from EPSRC grant number EP/L000776/1
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian
state space models (SSMs) is the intractability of estimating the system state.
Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced
more than two decades ago), provide numerical solutions to the nonlinear state
estimation problems arising in SSMs. When combined with additional
identification techniques, these algorithms provide solid solutions to the
nonlinear system identification problem. We describe two general strategies for
creating such combinations and discuss why SMC is a natural tool for
implementing these strategies.Comment: In proceedings of the 17th IFAC Symposium on System Identification
(SYSID). Added cover pag
State estimation for one-dimensional agro-hydrological processes with model mismatch
The importance of accurate soil moisture data for the development of modern
closed-loop irrigation systems cannot be overstated. Due to the diversity of
soil, it is difficult to obtain an accurate model for agro-hydrological system.
In this study, soil moisture estimation in 1D agro-hydrological systems with
model mismatch is the focus. To address the problem of model mismatch, a
nonlinear state-space model derived from the Richards equation is utilized,
along with additive unknown inputs. The determination of the number of sensors
required is achieved through sensitivity analysis and the orthogonalization
projection method. To estimate states and unknown inputs in real-time, a
recursive expectation maximization (EM) algorithm derived from the conventional
EM algorithm is employed. During the E-step, the extended Kalman filter (EKF)
is used to compute states and covariance in the recursive Q-function, while in
the M-step, unknown inputs are updated by locally maximizing the recursive
Q-function. The estimation performance is evaluated using comprehensive
simulations. Through this method, accurate soil moisture estimation can be
obtained, even in the presence of model mismatch
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
- …