Deterministic Dependency Structure Analyzer for Chinese

2005 ◽  
pp. 500-508 ◽  
Author(s):  
Yuchang Cheng ◽  
Masayuki Asahara ◽  
Yuji Matsumoto
Keyword(s):  
Dependency Structure ◽  
Structure Analyzer

MORTALITY FORECASTING WITH A SPATIALLY PENALIZED SMOOTHED VAR MODEL

2020 ◽  
pp. 1-29
Author(s):  
Le Chang ◽  
Yanlin Shi
Keyword(s):  
Population Data ◽  
Coefficient Matrix ◽  
Gradient Algorithm ◽  
Var Model ◽  
Vector Autoregressive ◽  
Mortality Forecasting ◽  
Dependency Structure ◽  
Svar Model ◽  
The Uk ◽  
Sparsity Structure

Abstract This paper investigates a high-dimensional vector-autoregressive (VAR) model in mortality modeling and forecasting. We propose an extension of the sparse VAR (SVAR) model fitted on the log-mortality improvements, which we name “spatially penalized smoothed VAR” (SSVAR). By adaptively penalizing the coefficients based on the distances between ages, SSVAR not only allows a flexible data-driven sparsity structure of the coefficient matrix but simultaneously ensures interpretable coefficients including cohort effects. Moreover, by incorporating the smoothness penalties, divergence in forecast mortality rates of neighboring ages is largely reduced, compared with the existing SVAR model. A novel estimation approach that uses the accelerated proximal gradient algorithm is proposed to solve SSVAR efficiently. Similarly, we propose estimating the precision matrix of the residuals using a spatially penalized graphical Lasso to further study the dependency structure of the residuals. Using the UK and France population data, we demonstrate that the SSVAR model consistently outperforms the famous Lee–Carter, Hyndman–Ullah, and two VAR-type models in forecasting accuracy. Finally, we discuss the extension of the SSVAR model to multi-population mortality forecasting with an illustrative example that demonstrates its superiority in forecasting over existing approaches.

Analysis of the volatility’s dependency structure during the subprime crisis

2013 ◽  
Vol 45 (36) ◽  
pp. 5031-5045 ◽  
Author(s):  
Bruno P. Arruda ◽  
Pedro L. Valls Pereira
Keyword(s):  
Subprime Crisis ◽  
Dependency Structure

scholarly journals A comparison of random walks in dependent random environments

2016 ◽  
Vol 48 (1) ◽  
pp. 199-214
Author(s):  
Werner R. W. Scheinhardt ◽  
Dirk P. Kroese
Keyword(s):  
Random Walks ◽  
Random Environment ◽  
Moving Average ◽  
Random Environments ◽  
Dependency Structure ◽  
Interesting Behavior

Abstract We provide exact computations for the drift of random walks in dependent random environments, including k-dependent and moving average environments. We show how the drift can be characterized and evaluated using Perron–Frobenius theory. Comparing random walks in various dependent environments, we demonstrate that their drifts can exhibit interesting behavior that depends significantly on the dependency structure of the random environment.

scholarly journals An autoregressive model for irregular time series of variable stars

2016 ◽  
Vol 12 (S325) ◽  
pp. 259-262
Author(s):  
Susana Eyheramendy ◽  
Felipe Elorrieta ◽  
Wilfredo Palma
Keyword(s):  
Time Series ◽  
Gravitational Lensing ◽  
Autoregressive Model ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Variable Stars ◽  
Light Curves ◽  
Finite Sample ◽  
Harmonic Model ◽  
Dependency Structure

AbstractThis paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.

scholarly journals Optimal reinsurance: a reinsurer’s perspective

2017 ◽  
Vol 12 (1) ◽  
pp. 147-184 ◽  
Author(s):  
Fei Huang ◽  
Honglin Yu
Keyword(s):  
At Risk ◽  
Closed Form ◽  
Value At Risk ◽  
Optimality Criteria ◽  
Total Loss ◽  
Optimal Reinsurance ◽  
Numerical Examples ◽  
Dependency Structure ◽  
Closed Form Solutions

AbstractIn this paper, the optimal safety loading that the reinsurer should set in the reinsurance pricing is studied, which is novel in the literature. It is first assumed that the insurer will choose the form of the reinsurance contract by following the results derived in Cai et al. Different optimality criteria from the reinsurer’s perspective are then studied, such as maximising the expectation of the profit, maximising the utility of the profit and minimising the value-at-risk of the reinsurer’s total loss. By applying the concept of comonotonicity, the problem in which the reinsurer is facing two risks with unknown dependency structure is also solved. Closed-form solutions are obtained when the underlying losses are zero-modified exponentially distributed. Finally, numerical examples are provided to illustrate the results derived.

scholarly journals Estimating extreme river discharges in Europe through a Bayesian Network

2016 ◽  
Author(s):  
Dominik Paprotny ◽  
Oswaldo Morales Nápoles
Keyword(s):  
Large Scale ◽  
Graphical Model ◽  
Flood Hazard ◽  
Physical Models ◽  
Method Performance ◽  
Discharge Data ◽  
Dependency Structure ◽  
Spatial Coverage ◽  
Geographical Characteristics ◽  
Better Than

Abstract. Large-scale hydrological modelling of flood hazard requires adequate extreme discharge data. Models based on physics are applied alongside those utilizing only statistical analysis. The former requires enormous computation power, while the latter are most limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian Networks (BN), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables describing the geographical characteristics of their catchments. Data on annual maxima of daily discharges from more than 1800 river gauge stations were collected, together with information on terrain, land use and climate of catchments that drain to those locations. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe, and better than a comparable global statistical method. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and is not affected by a split-sample validation. The BN was applied to a large domain covering all sizes of rivers in the continent, both for present and future climate, showing large variation in influence of climate change on river discharges, as well as large differences between emission scenarios. The method could be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

Semantic Labeling of Chinese Special Sentence Patterns Based on Automation Technology

2013 ◽  
Vol 427-429 ◽  
pp. 1649-1652
Author(s):  
Bo Chen ◽  
Chen Lv ◽  
Dong Hong Ji
Keyword(s):  
Semantic Information ◽  
Undirected Graph ◽  
Semantic Relations ◽  
Structure Theory ◽  
Dependency Structure ◽  
Semantic Labeling ◽  
Feature Structure ◽  
Sentence Patterns ◽  
Automation Technology ◽  
Novel Model

Parsing Chinese is a key issue in NLP. Many controversies arise from Chinese special sentence patterns. This paper puts forward a novel model Feature Structure theory to resolve the semantic labeling of Chinese special sentence patterns. We analyze the difficulties in annotating these sentences, and compare Feature Structure with dependency structure. Feature Structure represents more semantic information and more semantic relations. Feature Graph is a recursive undirected graph, allows nesting and multiple correlations.

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