Statistical Inference for Data Adaptive Target Parameters.

Area of Science:

• Statistical inference
• Data-driven science
• Machine learning

Background:

• Increasing reliance on data-driven research necessitates robust statistical methodologies.
• Existing pattern-finding methods often lack rigorous statistical grounding.
• Need for integrated approaches for exploratory and confirmatory data analysis.

Purpose of the Study:

• To develop a general, data-adaptive statistical target parameter framework.
• To provide a rigorous methodology for data-driven scientific discovery.
• To enhance statistical inference in the era of big data.

Main Methods:

• Partitioning samples into V equal-sized subsamples.
• Defining V splits for estimation and parameter-generating samples.
• Averaging V-sample specific target parameters to define the data-adaptive parameter.

Main Results:

• An estimator and central limit theorem for the data-adaptive target parameter.
• Demonstration of the methodology with practical examples and simulation studies.
• Validation through an analysis of adaptively determined intervention rules.

Conclusions:

• The data-adaptive target parameter approach offers a versatile framework for data-driven science.
• This methodology bridges exploratory analysis and confirmatory inference within a single dataset.
• Provides a foundation for increased statistical rigor in data-intensive research.