Development of new methods for the joint modeling of longitudinal and survival data with applications in finance and insurance
开发纵向数据和生存数据联合建模的新方法及其在金融和保险中的应用
基本信息
- 批准号:557209-2020
- 负责人:
- 金额:$ 5.44万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Alliance Grants
- 财政年份:2021
- 资助国家:加拿大
- 起止时间:2021-01-01 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
A large proportion of Canadian households find it challenging to manage their finance and insurance products. As a matter of fact, according to Statistics Canada, Canadian household credit market debt as a proportion of disposable income has increased to more than 176 % during the first quarter of 2020. Making sure that Canadians receive offers of products better suited to their specific needs at more opportune times would go a long way in improving their financial health. The goal of this project is to develop analytical tools that can help a major Canadian provider of financial and insurance products to identify the best products to offer to their clients and the best times at which to offer these products using the massive historical information that is available in the company's databases. While some statistical and machine learning algorithms that already exist can be tweaked to perform similar tasks, no method that fully takes into account the temporal, hierarchical and network structure of the data are currently available. In this project, such methods will be developed and will be implemented in open software that will be made publicly available. Because historical financial databases do not reflect the diversity of the current client base of financial companies in major Canadian cities, efforts will be made to make sure that the methods to be developed will be as efficient to improve the financial health of minority groups as they will be for majority groups.#(cr)#(lf)Une bonne proportion des ménages canadiens trouvent difficile de gérer leurs produits financiers et d'assurance. En effet, selon Statistique Canada, l'endettement des ménages canadiens en proportion du revenu disponible est passé à plus de 176% au cours du premier trimestre de 2020. S'assurer que les Canadiens reçoivent des offres de produits mieux adaptés à leurs besoins spécifiques à des moments plus opportuns contribuerait grandement à améliorer leur santé financière. L'objectif de ce projet est de développer des outils analytiques qui peuvent aider un grand fournisseur canadien de produits financiers et d'assurance à identifier les meilleurs produits à offrir à ses clients et les meilleurs moments pour offrir ces produits, en utilisant les informations historiques massives disponibles dans les bases de données de l'entreprise. Alors que certains algorithmes statistiques et d'apprentissage automatique qui existent déjà peuvent être modifiés pour effectuer des tâches similaires, aucune méthode qui prend pleinement en compte la structure temporelle, hiérarchique et en réseau des données n'est actuellement disponible. Dans ce projet, de telles méthodes seront développées et mises en oeuvre dans des logiciels ouverts qui seront disponibles au public. Parce que les bases de données financières historiques ne reflètent pas la diversité de la clientèle actuelle des entreprises financières dans les grandes villes canadiennes, des efforts seront faits pour s'assurer que les méthodes développées seront aussi efficaces pour améliorer la santé financière des groupes minoritaires qu'elles ne le seront pour améliorer celle des groupes majoritaires.
加拿大的很大一部分家庭发现管理其融资和保险产品是挑战。事实上,根据加拿大统计局的说法,加拿大家庭信贷市场债务是2020年第一季度的处置收入的一定比例,已增加到176%以上。确保加拿大人收到更适合其特定需求的产品在更多机会时期将对改善其财务健康有很长的路要走。该项目的目的是开发分析工具,可以帮助加拿大主要的金融和保险产品提供商确定最佳产品,以及使用公司数据库中可用的大量历史信息提供这些产品的最佳时期。尽管可以对已经存在的某些统计和机器学习算法进行调整以执行类似的任务,但目前可以完全考虑到数据的临时,层次结构和网络结构。在此项目中,将开发此类方法,并将在开放软件中实施,并将公开可用。由于历史金融数据库并不能反映加拿大主要城市中金融公司当前客户基础的多样性,因此将努力确保开发的方法将与对多数群体的财务状况相同。 Enhancements, selon statistique Canada, and the number of people who have lost their share of the financial situation, including the provisions of 176% of the total, and the number of people who have lost their share of the financial situation, including the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, such as the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, such as the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial situation, and the number of people who have lost their share of the financial情况,以及失去财务状况份额的人数,失去财务状况份额的人数,失去财务状况的份额,以及失去财务状况份额的人数以及失去财务状况的人数的人数,以及失去财务状况的人数,丧失财务状况的人数,以及失去财务状况的人数,丧失财务状况的人数,以及失去财务状况的人数,这些人的份额和财务状况的人数失去了一定的财务状况和人数 民众。重要的是要知道,了解金融服务的重要性以及保护公司免受公司金融服务的能力很重要。艺术的算法是在事务状态下创建的,以及现有结构的模拟效应的艺术的存在,例如临时的结构,艺术的结构以及艺术的分配。该组织有机会传播有关客户宏伟的消息,而那些努力进行其他人努力的人的努力将向公众公开。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Duchesne, Thierry其他文献
Inference methods for the conditional logistic regression model with longitudinal data
- DOI:
10.1002/bimj.200610379 - 发表时间:
2008-02-01 - 期刊:
- 影响因子:1.7
- 作者:
Craiu, Radu V.;Duchesne, Thierry;Fortin, Daniel - 通讯作者:
Fortin, Daniel
A general angular regression model for the analysis of data on animal movement in ecology
- DOI:
10.1111/rssc.12124 - 发表时间:
2016-04-01 - 期刊:
- 影响因子:1.6
- 作者:
Rivest, Louis-Paul;Duchesne, Thierry;Fortin, Daniel - 通讯作者:
Fortin, Daniel
Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification.
- DOI:
10.1080/02664763.2021.1911966 - 发表时间:
2022 - 期刊:
- 影响因子:1.5
- 作者:
Diop, S. Arona;Duchesne, Thierry;G. Cumming, Steven;Diop, Awa;Talbot, Denis - 通讯作者:
Talbot, Denis
Redefining the maximum sustainable yield for the Schaefer population model including multiplicative environmental noise
- DOI:
10.1016/j.jtbi.2008.04.025 - 发表时间:
2008-09-07 - 期刊:
- 影响因子:2
- 作者:
Bousquet, Nicolas;Duchesne, Thierry;Rivest, Louis-Paul - 通讯作者:
Rivest, Louis-Paul
Mixed conditional logistic regression for habitat selection studies
- DOI:
10.1111/j.1365-2656.2010.01670.x - 发表时间:
2010-05-01 - 期刊:
- 影响因子:4.8
- 作者:
Duchesne, Thierry;Fortin, Daniel;Courbin, Nicolas - 通讯作者:
Courbin, Nicolas
Duchesne, Thierry的其他文献
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{{ truncateString('Duchesne, Thierry', 18)}}的其他基金
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2021
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2020
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Development of new methods for the joint modeling of longitudinal and survival data with applications in finance and insurance
开发纵向数据和生存数据联合建模的新方法及其在金融和保险中的应用
- 批准号:
557209-2020 - 财政年份:2020
- 资助金额:
$ 5.44万 - 项目类别:
Alliance Grants
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2019
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2018
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2017
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Atelier de maillage en analyse de données, modélisation et aide à la décision
邮件工作室分析、建模和决策辅助
- 批准号:
505442-2016 - 财政年份:2016
- 资助金额:
$ 5.44万 - 项目类别:
Connect Grants Level 2
Inference and computational methods for mixed models with large or complex data
具有大量或复杂数据的混合模型的推理和计算方法
- 批准号:
RGPIN-2016-05883 - 财政年份:2016
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Statistical methods for longitudinal and censored or missing data
纵向和删失或缺失数据的统计方法
- 批准号:
227119-2010 - 财政年份:2015
- 资助金额:
$ 5.44万 - 项目类别:
Discovery Grants Program - Individual
Modélisation de l'incertitude prévisionnelle des processus hydrologiques via une modélisation des processus intrants au processus hydrologique
通过水文过程内部模型的水文过程不确定性预测模型
- 批准号:
479534-2015 - 财政年份:2015
- 资助金额:
$ 5.44万 - 项目类别:
Engage Grants Program
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