scholarly journals Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

2020 ◽  
Author(s):  
Kuk-Hyun Ahn
Keyword(s):  
Missing Values ◽  
The Other ◽  
Dependence Structure ◽  
Proposed Model ◽  
Pee Dee ◽  
Streamflow Estimation ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Pee Dee River ◽  
Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

scholarly journals Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

2021 ◽  
Vol 25 (8) ◽  
pp. 4319-4333
Author(s):  
Kuk-Hyun Ahn
Keyword(s):  
Missing Values ◽  
Resource Planning ◽  
The Other ◽  
Dependence Structure ◽  
Historical Case Study ◽  
Bivariate Model ◽  
Historical Case ◽  
Proposed Model ◽  
Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resource planning and management. This study proposes a multiple-dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high-dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple-dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin–Pee Dee River basin in the eastern USA. Both results inform that the proposed model is better suited for infilling missing values. To be specific, the proposed multiple-dependence model shows the improvement of 9.2 % on average compared to the bivariate model from the historical case study. The performance of the vine copula is further compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

scholarly journals Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10285
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Alaa Mohamd Shoukry ◽  
Mohammed Abdel Wahab Sharkawy ◽  
...  
Keyword(s):  
River Basin ◽  
Hybrid Model ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Dependence Structure ◽  
Absolute Deviation ◽  
River Inflow ◽  
Multi Scale ◽  
Proposed Model ◽  
Vine Copula

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

scholarly journals Examining the Interdependence between the Exchange Rates of China and ASEAN Countries: A Canonical Vine Copula Approach

2019 ◽  
Vol 11 (19) ◽  
pp. 5487 ◽  
Author(s):  
Liu ◽  
Wang ◽  
Sriboonchitta
Keyword(s):  
Exchange Rates ◽  
Tail Dependence ◽  
Dependence Structure ◽  
Positive Dependence ◽  
Asymmetric Dependence ◽  
Before And After ◽  
Copula Approach ◽  
Asean Countries ◽  
Vine Copula ◽  
Vine Copulas

Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.

scholarly journals Statistical Modeling of Insurance Data via Vine Copula

2019 ◽  
Author(s):  
Indranil GHOSH ◽  
Dalton Watts
Keyword(s):  
Structural Properties ◽  
Statistical Modeling ◽  
Life Insurance ◽  
Real Life ◽  
Dependence Structure ◽  
Flexible Tool ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Insurance Data

Copulas are useful tools for modeling the dependence structure between two or more variables. Copulas are becoming a quite flexible tool in modeling dependence among the components of a multivariate vector, in particular to predict losses in insurance and finance. In this article, we study the dependence structure of some well-known real life insurance data (with two components mainly) and subsequently identify the best bivariate copula to model such a scenario via VineCopula package in R. Associated structural properties of these bivariate copulas are also discussed.

scholarly journals Vine copulas structures modeling on Russian stock market

2019 ◽  
Vol 27 (4) ◽  
pp. 343-354 ◽  
Author(s):  
Eugeny Yu. Shchetinin
Keyword(s):  
Model Selection ◽  
Stock Market ◽  
High Accuracy ◽  
Dependence Structure ◽  
Rich Variety ◽  
The Rich ◽  
Russian Stock Market ◽  
Vine Copula ◽  
Vine Copulas ◽  
Complex Dependency

Pair-copula constructions have proven to be a useful tool in statistical modeling, particularly in the field of finance. The copula-based approach can be used to choose a model that describes the dependence structure and marginal behaviour of the data in efficient way, but is usually applied to pairs of securities. In contrast, vine copulas provide greater flexibility and permit the modeling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. So, to learn the best possible model, one has to identify the best possible structure, which necessitates identifying the connections between the variables and selecting between the multiple bivariate copulas for each pair in the structure. This paper features the use of regular vine copulas in analysis of the co-dependencies of four major Russian Stock Market securities such as Gazprom, Sberbank, Rosneft and FGC UES, represented by the RTS index. For these stocks the D-vine structures of bivariate copulas were constructed, which models are described by Gumbel, Student, BB1and BB7 copulas, and estimates of their parameters were obtained. Computer simulations showed a high accuracy of the approximation of the explored data by D-vine structure of bivariate copulas and the effectiveness of our approach in general.

scholarly journals Nonparametric estimation of simplified vine copula models: comparison of methods

2017 ◽  
Vol 5 (1) ◽  
pp. 99-120 ◽  
Author(s):  
Thomas Nagler ◽  
Christian Schellhase ◽  
Claudia Czado
Keyword(s):  
Nonparametric Estimation ◽  
Tail Dependence ◽  
Real Data ◽  
Parametric Models ◽  
Copula Models ◽  
Data Application ◽  
Models Comparison ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Vine Copulas

AbstractIn the last decade, simplified vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densities and bivariate copula densities. Besides parametric models, several approaches to nonparametric estimation of vine copulas have been proposed. In this article, we extend these approaches and compare them in an extensive simulation study and a real data application. We identify several factors driving the relative performance of the estimators. The most important one is the strength of dependence. No method was found to be uniformly better than all others. Overall, the kernel estimators performed best, but do worse than penalized B-spline estimators when there is weak dependence and no tail dependence.

scholarly journals Both Freud and Hoffman are Right: Anxious-Aggressive and Empathic Dimensions of Guilt

2008 ◽  
Vol 11 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Itziar Etxebarria ◽  
Pedro Apodaca
Keyword(s):  
Young Adults ◽  
Subjective Experience ◽  
Negative Emotions ◽  
The Other ◽  
Point Scale ◽  
Nested Models ◽  
Proposed Model ◽  
Better Than

The purpose of the study was to confirm a model which proposed two basic dimensions in the subjective experience of guilt, one anxious-aggressive and the other empathic, as well as another dimension associated but not intrinsic to it, namely, the associated negative emotions dimension. Participants were 360 adolescents, young adults and adults of both sexes. They were asked to relate one of the situations that most frequently caused them to experience feelings of guilt and to specify its intensity and that of 9 other emotions that they may have experienced, to a greater or lesser extent, at the same time on a 7-point scale. The proposed model was shown to adequately fit the data and to be better than other alternative nested models. This result supports the views of both Freud and Hoffman regarding the nature of guilt, contradictory only at a first glance.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

2021 ◽  
Vol 15 (12) ◽  
pp. 125
Author(s):  
Aya Taleb ◽  
Rizik M. H. Al-Sayyed ◽  
Hamed S. Al-Bdour
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Large Data ◽  
The Other ◽  
Jaccard Coefficient ◽  
Ensemble Technique ◽  
Signed Rank ◽  
Proposed Model ◽  
Different Populations ◽  
A New Technique

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

scholarly journals Prediction of Sudden Health Crises Owing to Congestive Heart Failure with Deep Learning Models

2021 ◽  
Vol 35 (1) ◽  
pp. 71-76
Author(s):  
Shaik Shabbeer ◽  
Edara Srinivasa Reddy
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Missing Values ◽  
Medical Knowledge ◽  
Data Generation ◽  
Clinical Disorder ◽  
Proposed Model ◽  
Service Efficiency ◽  
Mlp Model ◽  
Mimic Iii

Artificial Intelligence (AI) has its roots in every area in the present scenario. Healthcare is one of the markets in which AI has greatly grown in recent years. The tremendous increase in health data generation and the substantial evolution of the robust data analysis tools have contributed to AI improvement in health care and research, leading to increased service efficiency. Health reporting is stored as Electronic Health Records (EHR), providing information on the patients sought temporarily. EHR data have different issues, such as heterogeneity, missing values, distortion, noise, time, etc. This study reflects the irregularity of appointment that refers to the irregular timing of the operations (patient visits). Congestive heart failure (CHF) is a grave clinical disorder caused by an insufficient blood supply in the bloodstream owing to a heart muscle dysfunction. Most people suffer from CHF which result in death or immediate recognition. A multi-layer perceptron (MLP) model was used to treat visit stage abnormalities. The studies on the Medical Knowledge Mart for Intensive Care-III (MIMIC-III) dataset and the findings obtained indicate that the lack of a visit stage affects the estimation of the clinical outcome. It has been demonstrated that the readmission and reduction of the prediction model for mortality conditions is beneficial. Compared with baseline models, the proposed model is successful.

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