Explore the vast range of titles available.

Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combined model, the so-called factor tree copula model, with individual benefits from each of the two approaches. Rather than adding factors and causing computational problems and difficulties in interpretation and identification, a truncated vine structure is assumed on the residuals conditional on one or two latent variables. This structure can be better explained as a conditional dependence given a few interpretable latent variables. On the one hand, the parsimonious feature of factor models remains intact and any residual dependencies are being taken into account on the other. We discuss estimation along with model selection. In particular, we propose model selection algorithms to choose a plausible factor tree copula model to capture the (residual) dependencies among the item responses. Our general methodology is demonstrated with an extensive simulation study and illustrated by analyzing Post-Traumatic Stress Disorder.

It is a challenge to develop models that can represent the stochastic behaviour of rivers and basins. Currently used streamflow models were constructed under rigid hypotheses. Hence, these models are limited in their ability to represent nonlinear dependencies and/or unusual distributions. Copulas help overcome these limitations and are being employed widely for modelling hydrological data. For instance, pure copula-based models have been proposed to simulate univariate hydrological series. However, there have been few studies on the use of copulas to model multivariate inflow series. Thus, the aim of this study is to develop a pure copula-based model for simulating periodic multivariate streamflow scenarios, wherein temporal and spatial dependencies are considered. The model was employed in a set of 11 affluent natural energy series from Brazil. We used the model to simulate many scenarios and analyze them through statistical tests such as Levene’s test, the Kolmogorov-Smirnov test, Kupiec test, and t-test. In addition, we investigated the spatial and temporal dependence of the scenarios. Finally, the critical periods of the simulated scenarios were investigated. The results indicated that the proposed model is capable of simulating scenarios that preserve historical features observed in the original data.

It is evident that several real-life applications, such as telecommunication systems, call for the establishment of consecutive-type networks. Moreover, some of them require more complex connectors than the ones that exist already in the literature. Thereof, in the present work we provide a signature-based study of a reliability network consisting of identical m-consecutive-k-out-of-n: F structures with exchangeable components. The dependency of the components of each system is modeled with the aid of well-known Archimedean copulas. Exact formulae for determining the expected lifetime of the underlying reliability scheme are provided under different Archimedean copulas-based assumptions. Several numerical results are carried out to shed light on the performance of the resulting consecutive-type design. Some thoughts on extending the present study to more complex consecutive-type reliability structures are also discussed.

With the continuous increase of China’s foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China’s stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China’s stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China’s stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China’s industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages.

With the continuous development of Regional Integrated Energy System (RIES), demand response (DR) is composed of diversified loads including electric load, heat load and gas load. Their cross‐dependencies reflecting the nonlinear coupling complementary relationship between each load type are one of the key factors to improve multi‐energy flow optimisation modelling and utilisation efficiency on the demand side. Accordingly, this paper proposes a DR dependency assessment method considering electricity‐heat‐gas loads based on the BOXCOX‐Pair Copula model. BOXCOX transformation is introduced to convert various probability distribution statistics of electricity‐heat‐gas loads into Gaussian distributed variables. C‐vine Pair Copula is used to characterise an ensemble‐of‐trees of high‐dimensional dependency structure among multi‐energy demand modalities. Then the combined model of BOXCOX transformation and C‐vine Pair Copula can be employed to determine the complex coupling dependency among electricity‐heat‐gas loads according to different DR statistics. Some metrics between the original empirical distribution and BOXCOX‐Pair Copula distribution are introduced to assess the dependency evaluation precision of the proposed model. Finally, the novel dependency assessment model is numerically tested utilising the electricity load, heat load and gas load data sequences in a real RIES. The results illustrate that the cross‐dependency of electricity‐heat‐gas integrated DR based on the BOXCOX‐C‐vine copula model is closer to that of actual sample data, which verify the effectiveness and superiority of the proposed approach.

Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data.

The case study aims at selecting optimal bivariate copula models of the relationships between flood peaks and flood volumes from a regional perspective with a particular focus on flood generation processes. Besides the traditional approach that deals with the annual maxima of flood events, the current analysis also includes all independent flood events. The target region is located in the northwest of Austria; it consists of 69 small and mid-sized catchments. On the basis of the hourly runoff data from the period 1976- 2007, independent flood events were identified and assigned to one of the following three types of flood categories: synoptic floods, flash floods and snowmelt floods. Flood events in the given catchment are considered independent when they originate from different synoptic situations. Nine commonly-used copula types were fitted to the flood peak - flood volume pairs at each site. In this step, two databases were used: i) a process-based selection of all the independent flood events (three data samples at each catchment) and ii) the annual maxima of the flood peaks and the respective flood volumes regardless of the flood processes (one data sample per catchment). The goodness-of-fit of the nine copula types was examined on a regional basis throughout all the catchments. It was concluded that (1) the copula models for the flood processes are discernible locally; (2) the Clayton copula provides an unacceptable performance for all three processes as well as in the case of the annual maxima; (3) the rejection of the other copula types depends on the flood type and the sample size; (4) there are differences in the copulas with the best fits: for synoptic and flash floods, the best performance is associated with the extreme value copulas; for snowmelt floods, the Frank copula fits the best; while in the case of the annual maxima, no firm conclusion could be made due to the number of copulas with similarly acceptable overall performances. The general conclusion from this case study is that treating flood processes separately is beneficial; however, the usually available sample size in such real life studies is not sufficient to give generally valid recommendations for engineering design tasks.

Synthetic streamflow data is vital for the energy sector, as it feeds stochastic optimisation models that determine operational policies. Considered scenarios should differ from each other, but be the same from a statistical point of view, i.e., the scenarios must preserve features of the original time series such as the mean, variance, and temporal dependence structures. Traditionally, linear models are applied for this task. Recently, the advent of copulas has led to the emergence of an alternative that overcomes the drawbacks of linear models. In this context, we propose a methodology based on vine copulas for the stochastic simulation of periodic streamflow scenarios. Copula-based models that focus on single-site inflow simulation only consider lag-one time dependence. Therefore, we suggest an approach that incorporates lags that are greater than one. Furthermore, the proposed model deals with the strong periodicity that is commonly present in monthly streamflow time series. The resulting model is a non-linear periodic autoregressive model. Our results indicate that this model successfully simulates scenarios, preserving features that are observed in historical data.

We prove the identifiability property of Archimedean copula models for dependent competing risks data when at least one of the failure times is exponentially distributed. With this property, it becomes possible to quantify the dependence between competing events based on exponentially distributed dependent censored data. We demonstrate our estimation procedure using simulation studies and in an application to survival data.

Although email marketing is highly profitable and widely used by marketers, it has received limited attention in the marketing literature. Extant research has focused on either customers' email responses or the \"average\" effect of emails on purchases. In this article, the authors use data from a U.S. home improvement retailer to study customers' email open and purchase behaviors by using a unified hidden Markov and copula framework. Contrary to conventional wisdom, the authors find that email-active customers are not necessarily active in purchases, and vice versa. Furthermore, the number of emails sent by the retailer has a nonlinear effect on both the retailer's short- and long-term profitability. Through a counterfactual study, the authors provide a decision support system to guide retailers in making optimal email contact decisions. This study shows that sending the right number of emails is vital for long-term profitability. For example, sending four (ten) emails instead of the optimal number of seven emails can cause the retailer to lose 32% (16%) of its lifetime profit per customer.