9,356 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
Multiple imputation for continuous variables using a Bayesian principal component analysis
We propose a multiple imputation method based on principal component analysis
(PCA) to deal with incomplete continuous data. To reflect the uncertainty of
the parameters from one imputation to the next, we use a Bayesian treatment of
the PCA model. Using a simulation study and real data sets, the method is
compared to two classical approaches: multiple imputation based on joint
modelling and on fully conditional modelling. Contrary to the others, the
proposed method can be easily used on data sets where the number of individuals
is less than the number of variables and when the variables are highly
correlated. In addition, it provides unbiased point estimates of quantities of
interest, such as an expectation, a regression coefficient or a correlation
coefficient, with a smaller mean squared error. Furthermore, the widths of the
confidence intervals built for the quantities of interest are often smaller
whilst ensuring a valid coverage.Comment: 16 page
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
Don't Believe Everything You Hear : Preserving Relevant Information by Discarding Social Information
Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agentâs performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant informatio
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