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Author

Loukia Meligkotsidou

Other affiliations: Lancaster University
Bio: Loukia Meligkotsidou is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Autoregressive model & Quantile. The author has an hindex of 14, co-authored 41 publications receiving 788 citations. Previous affiliations of Loukia Meligkotsidou include Lancaster University.

Papers
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TL;DR: In order to enlarge the applicability of the model, inference for a multivariate Poisson model with larger structure is proposed, i.e. different covariance for each pair of variables, and extension to models with complete structure with many multi-way covariance terms is discussed.
Abstract: In recent years the applications of multivariate Poisson models have increased, mainly because of the gradual increase in computer performance. The multivariate Poisson model used in practice is based on a common covariance term for all the pairs of variables. This is rather restrictive and does not allow for modelling the covariance structure of the data in a flexible way. In this paper we propose inference for a multivariate Poisson model with larger structure, i.e. different covariance for each pair of variables. Maximum likelihood estimation, as well as Bayesian estimation methods are proposed. Both are based on a data augmentation scheme that reflects the multivariate reduction derivation of the joint probability function. In order to enlarge the applicability of the model we allow for covariates in the specification of both the mean and the covariance parameters. Extension to models with complete structure with many multi-way covariance terms is discussed. The method is demonstrated by analyzing a real life data set.

143 citations

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TL;DR: In this article, the authors examined finite mixtures of multivariate Poisson distributions as an alternative class of models for multivariate count data, allowing for both overdispersion in the marginal distributions and negative correlation, while they are computationally tractable using standard ideas from finite mixture modelling.

116 citations

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TL;DR: In this article, the authors introduce the idea of modeling the conditional quantiles of hedge fund returns using a set of risk factors and explore potential economic impacts of their approach by analysing hedge fund strategies return series and by constructing style portfolios.
Abstract: Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find evidence of model uncertainty in quantile regression models and evidence that different risk factors affect differently the tails of the distribution of hedge fund returns. We explore potential economic impacts of our approach by analysing hedge fund strategies return series and by constructing style portfolios.

81 citations

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TL;DR: The authors proposed a Bayesian approach to model comparison of different risk factor models that can be used for model averaging and found differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds.

72 citations

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TL;DR: The forward-backward algorithm as mentioned in this paper is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions, and it has been used to calculate the distribution of a sum of gamma random variables, and to simulate their joint distribution given their sum.
Abstract: Summary. The forward–backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, we show how the forward–backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward–backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.

53 citations


Cited by
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3,734 citations

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TL;DR: The method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes, and can be useful within an MCMC algorithm, even when the independence assumptions do not hold.
Abstract: We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes.

457 citations

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TL;DR: In this article, the authors examine how two types of earned media, traditional (e.g., publicity and press mentions) and online community posts, affect sales and activity in each other.
Abstract: Marketers distinguish between three types of media: paid (e.g., advertising), owned (e.g., company website), and earned (e.g., publicity). The effects of paid media on sales have been extensively covered in the marketing literature. The effects of earned media, however, have received limited attention. This paper examines how two types of earned media, traditional (e.g., publicity and press mentions) and social (e.g., blog and online community posts), affect sales and activity in each other. Fourteen months of daily sales and media activity data from a microlending marketplace website are analyzed using a multivariate autoregressive time series model. The authors find that (i) both traditional and social earned media affect sales, (ii) the per-event sales impact of traditional earned media activity is larger than for social earned media, (iii) however, because of the greater frequency of social earned media activity, after adjusting for event frequency social earned media’s sales elasticity is significantly greater than traditional earned media’s, and (iv) social earned media appears to play an important role in driving traditional earned media activity.

436 citations

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TL;DR: A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995.
Abstract: A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.

414 citations