348 research outputs found
A review of some models for the analysis of contingency tables : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Statistics at Massey University
Some models proposed for the analysis of contingency tables are reviewed and illustrated with examples.
These include standard loglinear models; models which are suitable for ordinal categorical variables such as ordinal loglinear, log multiplicative and logit models, and models based on an underlying distribution for the response; and models for incomplete and square tables.
Estimation methods and inference are also discussed
Functional Data Analysis of Payment Systems
In this paper for a credit cards payment system as robust predictor of transactions number and transactions intensity is proposed by means of functional autoregressive model. Intraday economic time series are treated as random continuous functions projected onto low dimensional subspace. Both B-splines and Fourier bases are considered for data smoothing
Gaussian process surrogates for failure detection: a Bayesian experimental design approach
An important task of uncertainty quantification is to identify {the
probability of} undesired events, in particular, system failures, caused by
various sources of uncertainties. In this work we consider the construction of
Gaussian {process} surrogates for failure detection and failure probability
estimation. In particular, we consider the situation that the underlying
computer models are extremely expensive, and in this setting, determining the
sampling points in the state space is of essential importance. We formulate the
problem as an optimal experimental design for Bayesian inferences of the limit
state (i.e., the failure boundary) and propose an efficient numerical scheme to
solve the resulting optimization problem. In particular, the proposed
limit-state inference method is capable of determining multiple sampling points
at a time, and thus it is well suited for problems where multiple computer
simulations can be performed in parallel. The accuracy and performance of the
proposed method is demonstrated by both academic and practical examples
Non-parametric inference of the population of compact binaries from gravitational wave observations using binned Gaussian processes
The observation of gravitational waves from multiple compact binary
coalescences by the LIGO-Virgo-KAGRA detector networks has enabled us to infer
the underlying distribution of compact binaries across a wide range of masses,
spins, and redshifts. In light of the new features found in the mass spectrum
of binary black holes and the uncertainty regarding binary formation models,
non-parametric population inference has become increasingly popular. In this
work, we develop a data-driven clustering framework that can identify features
in the component mass distribution of compact binaries simultaneously with
those in the corresponding redshift distribution, from gravitational wave data
in the presence of significant measurement uncertainties, while making very few
assumptions on the functional form of these distributions. Our generalized
model is capable of inferring correlations among various population properties
such as the redshift evolution of the shape of the mass distribution itself, in
contrast to most existing non-parametric inference schemes. We test our model
on simulated data and demonstrate the accuracy with which it can re-construct
the underlying distributions of component masses and redshifts. We also
re-analyze public LIGO-Virgo-KAGRA data from events in GWTC-3 using our model
and compare our results with those from some alternative parametric and
non-parametric population inference approaches. Finally, we investigate the
potential presence of correlations between mass and redshift in the population
of binary black holes in GWTC-3 (those observed by the LIGO-Virgo-KAGRA
detector network in their first 3 observing runs), without making any
assumptions about the specific nature of these correlations.Comment: Upload accepted versio
The Lag Structure and the General Effect of Ozone Exposure on Pediatric Respiratory Morbidity
Up to now no study has investigated the lag structure of children’s respiratory morbidity due to surface ozone. In the present study, we investigate the lag structure and the general effect of surface ozone exposure on children and adolescents’ respiratory morbidity using data from a particularly well suited area in southern Europe to assess the health effects of surface ozone. The effects of surface ozone are estimated using the recently developed distributed lag non-linear models, allowing for a relatively long timescale, while controlling for weather effects, a range of other air pollutants, and long and short term patterns. The public health significance of the estimated effects is higher than has been previously reported in the literature, providing evidence contrary to the conjecture that the surface ozone-morbidity association is mainly due to short-term harvesting. In fact, our data analysis reveals that the effects of surface ozone at medium and long timescales (harvesting-resistant) are substantially larger than the effects at shorter timescales (harvesting-prone), a finding that is consistent with all children and adolescents being affected by high surface ozone concentrations, and not just the very frail
Evolutionary Fitness in Variable Environments
One essential ingredient of evolutionary theory is the concept of fitness as
a measure for a species' success in its living conditions. Here, we quantify
the effect of environmental fluctuations onto fitness by analytical
calculations on a general evolutionary model and by studying corresponding
individual-based microscopic models. We demonstrate that not only larger growth
rates and viabilities, but also reduced sensitivity to environmental
variability substantially increases the fitness. Even for neutral evolution,
variability in the growth rates plays the crucial role of strongly reducing the
expected fixation times. Thereby, environmental fluctuations constitute a
mechanism to account for the effective population sizes inferred from genetic
data that often are much smaller than the census population size.Comment: main: 5 pages, 4 figures; supplement: 7 pages, 7 figue
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