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Yuan Wu - One of the best experts on this subject based on the ideXlab platform.

  • Serial Dependence Structure of Chinese outbound tourism demand and its contribution to forecasting
    2020
    Co-Authors: Yuan Wu
    Abstract:

    This working paper analyses the serial Dependence Structures of Chinese outbound tourism (COT) and its contribution to COT demand forecasting. Our research provides a flexible tool, namely copula method, to advance knowledge of serial Dependence Structure in COT demand and increase forecasting accuracy. Previous research usually employs forecasting models with predetermined Dependence Structure, mainly linear Structure, and imposes certain distribution assumption, mainly normal distribution, on the COT demand. In contrast, the copula method possesses greater flexibility as it provides numerous functions to account for the serial Dependence Structures and allows arbitrary distributions of COT demand. Our empirical findings reveal that the copula method can be used to specify various serial Dependence Structures of COT demand for different destinations and it performances better than ARIMA in forecasting. Besides methodological contributions, our study has marketing implication.

  • Analysis of tourism demand serial Dependence Structure for forecasting
    Tourism Economics, 2017
    Co-Authors: Yuan Wu
    Abstract:

    This study aims to extend knowledge of serial Dependence Structure in tourism demand modelling and make a contribution to tourism forecasting with the use of copula method. Analysis of serial Dependence can reveal the impact of current tourism demand on the future. This is important for tourism demand forecasting, as the prediction of future tourism demand relies highly on the historical demand information. However, serial Dependence, especially its Structure, has received very little attention in previous tourism research. The copula method is flexible as it provides various functions to specify different serial Dependence Structures and allows arbitrary distributions of tourism demand. We used five types of copulas to analyse two-dimensional serial Dependence Structure for 10 arrivals series to Singapore. The empirical findings show that serial Dependence Structures of arrivals can be non-linear. Additionally, the Student-t copula generates forecasts of tourism demand with higher accuracy than the autoregressive integrated moving average (ARIMA) and seasonal ARIMA models

Enkelejd Hashorva - One of the best experts on this subject based on the ideXlab platform.

  • extremes of gaussian random fields with regularly varying Dependence Structure
    Extremes, 2017
    Co-Authors: Krzysztof Debicki, Enkelejd Hashorva
    Abstract:

    Let \(X(t), t\in \mathcal {T}\) be a centered Gaussian random field with variance function σ 2(⋅) that attains its maximum at the unique point \(t_{0}\in \mathcal {T}\), and let \(M(\mathcal {T})=\sup _{t\in \mathcal {T}} X(t)\). For \(\mathcal {T}\) a compact subset of ℝ, the current literature explains the asymptotic tail behaviour of \(M(\mathcal {T})\) under some regularity conditions including that 1 − σ(t) has a polynomial decrease to 0 as t → t 0. In this contribution we consider more general case that 1 − σ(t) is regularly varying at t 0. We extend our analysis to Gaussian random fields defined on some compact set \(\mathcal {T}\subset \mathbb {R}^{2}\), deriving the exact tail asymptotics of \(M(\mathcal {T})\) for the class of Gaussian random fields with variance and correlation functions being regularly varying at t 0. A crucial novel element is the analysis of families of Gaussian random fields that do not possess locally additive Dependence Structures, which leads to qualitatively new types of asymptotics.

  • extremes of gaussian random fields with regularly varying Dependence Structure
    arXiv: Probability, 2016
    Co-Authors: Krzysztof Debicki, Enkelejd Hashorva
    Abstract:

    Let $X(t), t\in \mathcal{T}$ be a centered Gaussian random field with variance function $\sigma^2(\cdot)$ that attains its maximum at the unique point $t_0\in \mathcal{T}$, and let $M(\mathcal{T}):=\sup_{t\in \mathcal{T}} X(t)$. For $\mathcal{T}$ a compact subset of $\R$, the current literature explains the asymptotic tail behaviour of $M(\mathcal{T})$ under some regularity conditions including that $1- \sigma(t)$ has a polynomial decrease to 0 as $t \to t_0$. In this contribution we consider more general case that $1- \sigma(t)$ is regularly varying at $t_0$. We extend our analysis to random fields defined on some compact $\mathcal{T}\subset \R^2$, deriving the exact tail asymptotics of $M(\mathcal{T})$ for the class of Gaussian random fields with variance and correlation functions being regularly varying at $t_0$. A crucial novel element is the analysis of families of Gaussian random fields that do not possess locally additive Dependence Structures, which leads to qualitatively new types of asymptotics.

José C. Príncipe - One of the best experts on this subject based on the ideXlab platform.

  • Analyzing Dependence Structure of the human brain in response to visual stimuli
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Bilal H. Fadlallah, Sohan Seth, Andreas Keil, José C. Príncipe
    Abstract:

    Communication between cortices mediated by deep brain Structures such as the amygdala and fusiform gyrus has been suggested to explain the enhanced perception of stimuli bearing emotional content or having facial features. In this paper, we analyze the Dependence Structure of the relevant brain regions to assess their connectivity in response to a facial stimulus, and to discriminate it from a mock stimulus. The proposed approach treats the brain as a graphical network where vertices correspond to locations of electroencephalogram (EEG) recordings, and weights of the edges correspond to Dependence values. We employ a novel measure of Dependence, called generalized measure of association (GMA), due to its underlying simplicity, and compare its performance against Pearson's correlation. The performance is assessed in terms of the discriminability between the face and mock stimuli. We observe that GMA successfully exhibits higher Dependence in regions that might reflect the activity of the amygdaloid complex and the right fusiform gyrus when the stimulus is face. Furthermore, the distributions of the Dependence values show that GMA also achieves a better separation between face and mock, compared to correlation.

  • An Association Framework to Analyze Dependence Structure in Time Series
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Bilal H. Fadlallah, Sohan Seth, Andreas Keil, Austin J. Brockmeier, Lin Li, José C. Príncipe
    Abstract:

    The purpose of this paper is two-fold: first, to propose a modification to the generalized measure of association (GMA) framework that reduces the effect of temporal Structure in time series; second, to assess the reliability of using association methods to capture Dependence between pairs of EEG channels using their time series or envelopes. To achieve the first goal, the GMA algorithm was updated so as to minimize the effect of the correlation inherent in the time Structure. The reliability of the modified scheme was then assessed on both synthetic and real data. Synthetic data was generated from a Clayton copula, for which null hypotheses of uncorrelatedness were constructed for the signal. The signal was processed such that the envelope emulated important characteristics of experimental EEG data. Results show that the modified GMA procedure can capture pairwise Dependence between generated signals as well as their envelopes with good statistical power. Furthermore, applying GMA and Kendall's tau to quantify Dependence using the extracted envelopes of processed EEG data concords with previous findings using the signal itself.

Chiraz Labidi - One of the best experts on this subject based on the ideXlab platform.

  • Return Interval, Dependence Structure and Multivariate Normality
    2020
    Co-Authors: Chiraz Labidi
    Abstract:

    We focus on changes in the multivariate distribution of index returns stemming purely from varying the return interval, assuming daily to quarterly returns. Whereas longtailedness is present in daily returns, we find that, in agreement with a well-established idea, univariate return distributions converge to normality as the return interval is lengthened. Such convergence does not occur, however, for multivariate distributions. Using a new method to parametrically model the Dependence Structure implying negative asymptotic Dependence in return series is the reason for the rejection of multivariate normality for low return frequencies.

  • Return Interval, Dependence Structure and Multivariate Normality
    Journal of Economics and Finance, 2004
    Co-Authors: Chiraz Labidi
    Abstract:

    We focus on changes in the multivariate distribution of index returns stemming purely from varying the return interval, assuming daily to quarterly returns. Whereas long-tailedness is present in daily returns, we find that, in agreement with a well-established idea, univariate return distributions converge to normality as the return interval is lengthened. Such convergence does not occur, however, for multivariate distributions. Using a new method to parametrically model the Dependence Structure of stock index returns, we show that the persistence of a Dependence Structure implying negative asymptotic Dependence in return series is the reason for the rejection of multivariate normality for low return frequencies. Copyright Academy of Economics and Finance 2004

Bengt Sandkull - One of the best experts on this subject based on the ideXlab platform.

  • the use of Dependence Structure matrix and domain mapping matrix in managing uncertainty in multiple project situations
    International Journal of Project Management, 2005
    Co-Authors: Mike Danilovic, Bengt Sandkull
    Abstract:

    Development of complex products is performed in multi-project environment in which it is crucial to explore interdependencies and manage the uncertainty with the information exchange and the understanding of the context. The purpose of this paper is to introduce a Dependence Structure matrix and domain mapping matrix approach that enables the systematic identification of interdependencies and relations in a Multi-project environment. These approaches enables clarifications of assumptions, the tractability of dependencies, explores the information needed within and between different departments, projects and people. This creates a transparency and enables the synchronization of actions through transformation of information and exploration of assumptions within and between domains. The outcomes of this process are situational visibility creating direction and accountability and the learning that takes place through communicating, reflecting, understanding, and acting.