JC
J. Cai
26 records found
1
Authored
Temporal bipartite networks that describe how users interact with tasks or items over time have recently become available. Such temporal information allows us to explore user behavior in-depth. We propose two metrics, the relative switch frequency and distraction in time to me ...
Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile regression and an extrapolation technique from extreme value theory, we develop an estimator for c ...
We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, w ...
The estimation of high quantiles for very low probabilities of exceedance pn much smaller than 1/n (with n the sample size) remains a major challenge. For this purpose, the log-Generalized Weibull (log-GW) tail limit was recently proposed as regularity condition as an alternative
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We propose an estimator of the marginal expected shortfall by considering a log transformation of a variable which has an infinite expectation. We establish the asymptotic normality of our estimator under general assumptions. A simulation study suggests that the estimation proced
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One of Risso’s dolphin’s distinctive characteristics is the tendency to “lighten” with age due to the accumulation of unpigmented scars. These accumulated scars may provide an indication of age. Photographic skin recaptures gathered from 61 free-ranging animals over a period of 1
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Applying extreme value statistics in meteorology and environmental science requires accurate estimators on extreme value indices that can be around zero. Without having prior knowledge on the sign of the extreme value indices, the probability weighted moment (PWM) estimator is a
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When considering d possibly dependent random variables, one is often interested in extreme risk regions, with very small probability p. We consider risk regions of the form {z ∈ ℝd : f(z) ≤ β}, where f is the joint density and β a small number. Estimation of such an extreme risk
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In this paper, we provide an asymptotic expression for mean integrated squared error (MISE) of nonlinear wavelet density estimator for a truncation model. It is assumed that the lifetime observations form a stationary α-mixing sequence. Unlike for kernel estimator, the MISE expre
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Contributed
The most common way to evaluate traffic safety is investigating the occurrence and severity of crashes using historical data. This approach however has a number of limitations, the most important of which is probably its reactive nature. An alternative method using non-crash even
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In this thesis we are going to study outlier detection methods and propose a new method. Classical outlier detection is typically based on the assumption that the data is from a Gaussian/normal distribution. When the underlying distribution of a random sample is heavy tailed, so
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Three interest rate models are researched: Displaced Exponential-Vasicek, Hull-White one factor and Hull-White two factors with time-dependent volatility parameters. The motivation for this is two-fold: firstly, we would like to understand how the capital calculations would be im
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Precipitation has high spatial and temporal uncertainty, which makes it challenging to predict. We focus specifically on extreme amounts of precipitation. The Royal Dutch Meteorological Institute (KNMI) uses a numerical model, approximating the solutions to partial differential e
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Characterizations of Multivariate Tail Dependence
On theory and inference to assess extremal dependence structures
This thesis gathers, develops and evaluates several characterizations of multivariate tail dependence. It is established that the stable tail dependence function (STDF) is a suitable copula-based dependence function that fully captures the multivariate extremal dependence structu
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Over the past three decades, the singular value decomposition has been increasingly used for various big data applications. As it allows for rank reduction of the input data matrix, it is not only able to compress the information contained, but can even reveal underlying patterns
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This thesis project developed an alternative PM2.5 concentration prediction model and early warning system of extreme air pollution based on the long short-term memory (LSTM) and achieved satisfying performance. To research more deeply, we divided the task into two parts. The fir
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Voorspellen van veel neerslag
Gebruikmakend van regressieanalyse en de ECMWF-modeluitvoer
In deze scriptie is onderzocht of regressie, een postprocessing methode, een goede toevoeging is aan het model dat het Europees Centrum voor Weersverwachtingen op Middellange Termijn (ECMWF) ontwikkeld heeft en waarvan het KNMI de uitvoer gebruikt, specifiek om te bepalen of er
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Intraday liquidity risk estimation using transaction data
An extreme value theory approach
Intraday liquidity risk is a subject that applies to all banks, and arises whenever there is a timing mismatch between incoming and outgoing payments within a business day. In case such a mismatch occurs, the bank is exposed to the risk that it is unable to meet its payment oblig
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