scholarly journals JOINT MODEL PREDICTION AND APPLICATION TO INDIVIDUAL-LEVEL LOSS RESERVING

2021 ◽  
pp. 1-26
Author(s):  
A. Nii-Armah Okine ◽  
Edward W. Frees ◽  
Peng Shi
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Model Framework ◽  
Modeling Framework ◽  
Property Insurance ◽  
Individual Level ◽  
Proposed Model ◽  
Out Of Sample ◽  
Loss Reserving ◽  
Using Data

Abstract Innon-life insurance, the payment history can be predictive of the timing of a settlement for individual claims. Ignoring the association between the payment process and the settlement process could bias the prediction of outstanding payments. To address this issue, we introduce into the literature of micro-level loss reserving a joint modeling framework that incorporates longitudinal payments of a claim into the intensity process of claim settlement. We discuss statistical inference and focus on the prediction aspects of the model. We demonstrate applications of the proposed model in the reserving practice with a detailed empirical analysis using data from a property insurance provider. The prediction results from an out-of-sample validation show that the joint model framework outperforms existing reserving models that ignore the payment–settlement association.

scholarly journals Forecasting Realized Volatility Using a Nonnegative Semiparametric Model

2019 ◽  
Vol 12 (3) ◽  
pp. 139 ◽  
Author(s):  
Anders Eriksson ◽  
Daniel P. A. Preve ◽  
Jun Yu
Keyword(s):  
Predictive Accuracy ◽  
Estimation Method ◽  
Semiparametric Model ◽  
Error Component ◽  
New Model ◽  
Finite Samples ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Using Data

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that the distributional and functional form of its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new method and suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts.

scholarly journals Joint Modeling of Longitudinal Markers and Time-to-Event Outcomes: An Application and Tutorial in Patients After Surgical Repair of Transposition of the Great Arteries

2021 ◽  
Author(s):  
Sara J. Baart ◽  
Roel L.F. van der Palen ◽  
Hein Putter ◽  
Roula Tsonaka ◽  
Nico A. Blom ◽  
...  
Keyword(s):  
Surgical Repair ◽  
Cox Model ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Time Dependent ◽  
Valve Regurgitation ◽  
Time To Event ◽  
Modeling Framework

Background: Most patients with congenital heart disease survive into adulthood; however, residual abnormalities remain and management of the patients is life-long and personalized. Patients with surgical repair of transposition of the great arteries, for example, face the risk to develop neoaortic valve regurgitation. Cardiologists update the prognosis of the patient intuitively with updated information of the cardiovascular status of the patient, for instance from echocardiographic imaging. Methods: Usually a time-dependent version of the Cox model is used to analyze repeated measurements with a time-to-event outcome. New statistical methods have been developed with multiple advantages, of which the most prominent one being the joint model for longitudinal and time-to-event outcome. In this tutorial, the joint modeling framework is introduced and applied to patients with transposition of the great arteries after surgery with a long-term follow-up, where repeated echocardiographic values of the neoaortic root are evaluated against the risk of neoaortic valve regurgitation. Results: The data are analyzed with the time-dependent Cox model as benchmark method, and the results are compared with a joint model, leading to different conclusions. The flexibility of the joint model is shown by adding the growth rate of the neoaortic root to the model and adding repeated values of body surface area to obtain a multimarker model. Lastly, it is demonstrated how the joint model can be used to obtain personalized dynamic predictions of the event. Conclusions: The joint model for longitudinal and time-to-event data is an attractive method to analyze data in follow-up studies with repeated measurements. Benefits of the method include using the estimated natural trajectory of the longitudinal outcome, great flexibility through multiple extensions, and dynamic individualized predictions.

A MARKED COX MODEL FOR THE NUMBER OF IBNR CLAIMS: ESTIMATION AND APPLICATION

2019 ◽  
Vol 49 (03) ◽  
pp. 709-739 ◽  
Author(s):  
Andrei L. Badescu ◽  
Tianle Chen ◽  
X. Sheldon Lin ◽  
Dameng Tang
Keyword(s):  
Cox Model ◽  
Computational Cost ◽  
Claim Data ◽  
Estimation Methods ◽  
Arrival Process ◽  
Data Set ◽  
Cox Processes ◽  
Proposed Model ◽  
Out Of Sample ◽  
Loss Reserving

AbstractIncurred but not reported (IBNR) loss reserving is of great importance for Property & Casualty (P&C) insurers. However, the temporal dependence exhibited in the claim arrival process is not reflected in many current loss reserving models, which might affect the accuracy of the IBNR reserve predictions. To overcome this shortcoming, we proposed a marked Cox process and showed its many desirable properties in Badescu et al. (2016).In this paper, we consider the model estimation and applications. We first present an expectation–maximization (EM) algorithm which guarantees the efficiency of the estimators unlike the moment estimation methods widely used in estimating Cox processes. In addition, the proposed fitting algorithm can be implemented at a reasonable computational cost. We examine the performance of the proposed algorithm through simulation studies. The applicability of the proposed model is tested by fitting it to a real insurance claim data set. Through out-of-sample tests, we find that the proposed model can provide realistic predictive distributions.

scholarly journals Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Behnaz Alafchi ◽  
Hossein Mahjub ◽  
Leili Tapak ◽  
Ghodratollah Roshanaei ◽  
Mohammad Ali Amirzargar
Keyword(s):  
Renal Transplantation ◽  
Joint Modeling ◽  
Joint Model ◽  
Single Event ◽  
Two Stage ◽  
Longitudinal Outcomes ◽  
Proposed Model ◽  
Longitudinal Biomarker ◽  
Multivariate Longitudinal Outcomes ◽  
Multistate Process

In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.

Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

2018 ◽  
Vol 27 (2) ◽  
pp. 208-222 ◽  
Author(s):  
Shahryar Minhas ◽  
Peter D. Hoff ◽  
Michael D. Ward
Keyword(s):  
Network Analysis ◽  
Graph Model ◽  
Linear Modeling ◽  
Model Framework ◽  
Modeling Framework ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Wide Range ◽  
Out Of Sample ◽  
Latent Factor Models

We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is (a) to be easy to implement; (b) interpretable in a general linear model framework; (c) computationally straightforward; (d) not prone to degeneracy; (e) captures first-, second-, and third-order network dependencies; and (f) notably outperforms ERGMs and LSMs on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.

scholarly journals A Bayesian Framework for Functional Mapping through Joint Modeling of Longitudinal and Time-to-Event Data

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Kiranmoy Das ◽  
Runze Li ◽  
Zhongwen Huang ◽  
Junyi Gai ◽  
Rongling Wu
Keyword(s):  
Developmental Process ◽  
Joint Modeling ◽  
Joint Model ◽  
Functional Mapping ◽  
Time To Event ◽  
Statistical Research ◽  
Modeling Framework ◽  
Modern Biology ◽  
Developmental Processes ◽  
Event Time

The most powerful and comprehensive approach of study in modern biology is to understand the whole process of development and all events of importance to development which occur in the process. As a consequence, joint modeling of developmental processes and events has become one of the most demanding tasks in statistical research. Here, we propose a joint modeling framework for functional mapping of specific quantitative trait loci (QTLs) which controls developmental processes and the timing of development and their causal correlation over time. The joint model contains two submodels, one for a developmental process, known as a longitudinal trait, and the other for a developmental event, known as the time to event, which are connected through a QTL mapping framework. A nonparametric approach is used to model the mean and covariance function of the longitudinal trait while the traditional Cox proportional hazard (PH) model is used to model the event time. The joint model is applied to map QTLs that control whole-plant vegetative biomass growth and time to first flower in soybeans. Results show that this model should be broadly useful for detecting genes controlling physiological and pathological processes and other events of interest in biomedicine.

scholarly journals Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

2017 ◽  
Vol 28 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Data ◽  
Functional Data ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Joint Models ◽  
Modeling Framework ◽  
Dynamic Prediction

In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative study.

It Is Your Decision to Date Interracially: The Influence of Family Approval on the Likelihood of Interracial/Interethnic Dating

2021 ◽  
pp. 0192513X2199413
Author(s):  
Byron Miller ◽  
Savanah Catalina ◽  
Sara Rocks ◽  
Kathryn Tillman
Keyword(s):  
College Students ◽  
Young Adults ◽  
Romantic Relationships ◽  
Family Members ◽  
Individual Level ◽  
Interracial Romance ◽  
Using Data ◽  
Do So

Although attitudes toward interracial romantic relationships (IRRs) have generally improved over the years, many Americans still disapprove of their family members being in IRRs. Prior studies have examined correlates of individual-level attitudes about interracial romance, but less is known about whether family members’ attitudes are directly associated with young people’s decisions to date interracially. Using data collected from 790 romantically involved college students at two large public four-year universities, we find that young adults who believe their siblings, parents, and grandparents approve of IRRs have greater odds of dating interracially. Compared to Whites, Blacks and Hispanics are more likely to be interracially involved but their decision to do so is much less dependent on the approval of their parents and grandparents. We also find young adults are more likely to date interracially if they have five or more relatives with IRR experience themselves. The findings and their implications are discussed.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

Hierarchical Concept-Driven Language Model

2021 ◽  
Vol 15 (6) ◽  
pp. 1-22
Author(s):  
Yashen Wang ◽  
Huanhuan Zhang ◽  
Zhirun Liu ◽  
Qiang Zhou
Keyword(s):  
Language Model ◽  
Generation Process ◽  
Data Generation ◽  
Modeling Framework ◽  
Long Distance ◽  
Short Text ◽  
Proposed Model ◽  
Scalable Inference ◽  
End To End ◽  
Hidden Layer

For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.

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