While many of the prevalent stochastic mortality models provide adequate short- to medium-term forecasts, only few provide biologically plausible descriptions of mortality on longer horizons and are sufficiently stabl...
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While many of the prevalent stochastic mortality models provide adequate short- to medium-term forecasts, only few provide biologically plausible descriptions of mortality on longer horizons and are sufficiently stable to be of practical use in smaller populations. Among the very first to address the issue of modelling adult mortality in small populations was the SAINT model, which has been used for pricing, reserving and longevity risk management by the Danish Labour Market Supplementary Pension Fund (ATP) for more than a decade. The lessons learned have broadened our understanding of desirable model properties from the practitioner's point of view and have led to a revision of model components to address accuracy, stability, flexibility, explainability and credibility concerns. This paper serves as an update to the original version published 10 years ago and presents the SAINT model with its modifications and the rationale behind them. The main improvement is the generalization of frailty models from deterministic structures to a flexible class of stochastic models. We show by example how the SAINT framework is used for modelling mortality at ATP and make comparisons to the Lee-Carter model.
In survival analysis, cure models have gained much importance due to rapid advancements in medical sciences. More recently, a subset of cure models, called destructive cure models, have been studied extensively under ...
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In this study we present the model-based clustering in order to overcome the problem of mixed pixels for satellite imagery. The mixed pixel problem is one of the major reasons that affect the classification accuracy i...
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In this study we present the model-based clustering in order to overcome the problem of mixed pixels for satellite imagery. The mixed pixel problem is one of the major reasons that affect the classification accuracy in the classification of remotely sensed images. Mixed pixels are usually the prime reason for degrading the success in image classification and object recognition. A modified model-based clustering algorithm is developed by modifying membership function and compared with the traditional model-based clustering algorithm in terms of classification error and brier score. Results on classification of satellite images reveal that the suggestive algorithms are robust and effective.
Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operationa...
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ISBN:
(纸本)9781665453554
Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operational efficiency, they also expose the physical system to cyber-attacks. In this paper, we consider the problem of transmission system state estimation based on measurements from a number of PMUs. In this context, a PMU data integrity attack, Man-in-the-Middle (MitM) attack that can potentially cause a severe impact on the grid is analyzed. Specifically, we propose a novel method based on an alternate expectation-maximization framework to mitigate the effects of these attacks on the state estimation process. Numerical tests are conducted on IEEE-14, 30 and 118 bus systems with different attack scenarios to validate the developed method. Unlike existing works, the proposed algorithm provides accurate state estimates without any prior knowledge of the location of the attack or the number of meters being attacked.
In this paper, we develop a new survival model induced by dis-crete frailty with Katz distribution. The new model encompasses as partic-ular cases the mixture cure rate model and promotion cure rate model and has a pr...
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In this paper, we develop a new survival model induced by dis-crete frailty with Katz distribution. The new model encompasses as partic-ular cases the mixture cure rate model and promotion cure rate model and has a proportional hazards structure when the covariates are modeled through mean frailty. Furthermore, we construct a regression model to evaluate the ef-fects of covariates on both the cured fraction and risk of the event of interest. We discuss inference aspects of the proposed model in a classical approach, where an expectation maximization algorithm is developed to determine the maximum likelihood estimates of the models parameters. Finally, the model is fully illustrated with a dataset on cervical cancer.
This paper introduces a semi-supervised learning technique for model-based clustering. Our research focus is on applying it to matrices of ordered categorical response data, such as those obtained from the surveys wit...
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We introduce a novel modeling framework-the Hawkes(p, q) process-which allows us to parsimoniously disentangle and quantify the time-varying share of high frequency financial price changes that are due to endogenous f...
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We introduce a novel modeling framework-the Hawkes(p, q) process-which allows us to parsimoniously disentangle and quantify the time-varying share of high frequency financial price changes that are due to endogenous feedback processes and not exogenous impulses. We show how both flexible exogenous arrival intensities, as well as a time-dependent feedback parameter can be estimated in a structural manner using an Expectation Maximization algorithm. We use this approach to investigate potential characteristic signatures of anomalous market regimes in the vicinity of 'flash crashes'-events where prices exhibit highly irregular and cascading dynamics. Our study covers some of the most liquid electronic financial markets, in particular equity and bond futures, foreign exchange and cryptocurrencies. Systematically balancing the degrees of freedom of both exogenously driving processes and endogenous feedback variation using information criteria, we show that the dynamics around such events are not universal, highlighting the usefulness of our approach: (i) post-mortem, for developing remedies and better future processes-e.g. improving circuit breakers or latency floor designs-and potentially (ii) ex-ante, for short-term forecasts in the case of endogenously driven events. Finally, we test our proposed model against a process with refined treatment of exogenous clustering dynamics in the spirit of the recently proposed autoregressive moving-average (ARMA) point process.
Scale mixtures of normal distributions are useful for statistical procedures involving symmetric and heavy-tailed data. Ferreira, Lachos, and Bolfarine (2016) defined a multivariate skewed version of these distributio...
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Scale mixtures of normal distributions are useful for statistical procedures involving symmetric and heavy-tailed data. Ferreira, Lachos, and Bolfarine (2016) defined a multivariate skewed version of these distributions that offers much-needed flexibility by combining both skewness and heavy tails. In this work, we develop a linear mixed model based on skew scale mixtures of normal distributions, with emphasis on the skew Student-tnormal, skew-slash and skew-contaminated normal distributions. Using the hierarchical structure of the model, we develop maximum likelihood estimation of the model parameters via the expectation-maximization (em) algorithm. In addition, the standard errors are obtained via the approximate information matrix and the local influence analysis is explored under some perturbation schemes. To examine the performance and the usefulness of the proposed method, we present simulation studies and analyze a real dataset.
We consider the problem of estimating the parameters a Gaussian Mixture Model with K components of known weights, all with an identity covariance matrix. We make two contributions. First, at the population level, we p...
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We consider the problem of estimating the parameters a Gaussian Mixture Model with K components of known weights, all with an identity covariance matrix. We make two contributions. First, at the population level, we present a sharper analysis of the local convergence of em and gradient em, compared to previous works. Assuming a separation of Omega(root log K), we prove convergence of both methods to the global optima from an initialization region larger than those of previous works. Specifically, the initial guess of each component can be as far as (almost) half its distance to the nearest Gaussian. This is essentially the largest possible contraction region. Our second contribution are improved sample size requirements for accurate estimation by em and gradient em. In previous works, the required number of samples had a quadratic dependence on the maximal separation between the K components, and the resulting error estimate increased linearly with this maximal separation. In this manuscript we show that both quantities depend only logarithmically on the maximal separation.
The Philippines, as part of the Circum-Pacific belt, is considered as one of the most seismically active countries in the world. Earthquake occurrence is frequent and its effects vary depending on its size. Understand...
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The Philippines, as part of the Circum-Pacific belt, is considered as one of the most seismically active countries in the world. Earthquake occurrence is frequent and its effects vary depending on its size. Understanding how the occurrences happen is therefore important. Stochastic models of earthquake occurrence have been used to study seismic activities in various active earthquake zones globally. In this paper, we apply Poisson hidden Markov models (PHMM) using the January 1, 1960 to January 20, 2019 earthquake data of Metro Manila, Philippines. The parameters in the models are estimated using expectation-maximization (em) algorithm. We determine using various statistical tests that the 5-state PHMM best represents the earthquake data and implement bootstrap algorithm to validate the acceptability of its parameter estimates. Moreover, we investigate the forecasting capability of the 5-state PHMM by comparing it to the ARIMA model. Using unscaled mean bounded relative absolute error (UMBRAE), we find that the 5-state PHMM gives closer one-step ahead forecasts and is a better forecasting model for the considered data.
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