Heteroscedastic trees, ensembles, and other joint models for means and variances

均值和方差的异方差树、集成和其他联合模型

• 批准号: 342205-2013
• 负责人: Loughin, Thomas
• 金额: $ 1.09万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572474
• 关键词: Heteroscedastic; trees; ensembles; other; joint

Predicting new outcomes based on data is an important part of any scientific, economic, or business endeavour. There are numerous statistical tools available today that allow people to predict mean, or average, responses to new situations. However, such tools are not yet designed to predict variability, which in some situations is just as important as predicting the average. In manufacturing, software design, chemistry, and economics, understanding how variable or volatile a future outcome may be is often the primary goal of a data analysis. Development of tools for performing this analysis has lagged behind that for predicting means. This proposal aims to develop methods of data analysis that can predict variability instead of, or in addition to, predicting an average. We will start from modern tools called "regression trees" that are already in heavy use for predicting means, and improve and adapt them to increase their capabilities. We will also assess how well the ordinary, unimproved tools can complete their task of prediction when faced with data that exhibit systematic changes in variablility, which is a situation that they are not meant to handle. If they are not already robust against these changes in variability, we will improve these tools in another way that will allow them to work better.

基于数据预测新结果是任何科学、经济或商业努力的重要组成部分。今天有许多可用的统计工具，可以让人们预测对新情况的平均反应。然而，这些工具还没有被设计用来预测可变性，在某些情况下，可变性和预测平均值一样重要。在制造业、软件设计、化学和经济学中，了解未来结果的变化或不稳定性通常是数据分析的主要目标。执行这种分析的工具的开发落后于预测手段的开发。