Calibrating Subject Data Using Partitioning and Differential Weighting
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Solution Overview
Problem
Current methods for population estimation using data sets without well-defined relationships to the target population are inaccurate due to selection biases and the absence of essential calibration variables, particularly in modern data privacy constraints where personal information is limited or not available.
Innovation Solution
A system and method that calibrate subject data sets using a differential weighting scheme and partitioning schemes to align with reference data sets, allowing for accurate representation of the target population without requiring personal information or calibration variables present in the subject data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is collected from non-probability samples or data sets without well-defined relationships to the target population, then data collection cost and time are reduced, but measurement precision and reliability of population estimates deteriorate due to selection biases
Solution Approach 1:
The patent introduces calibration variables as intermediary elements that bridge the subject data set and the target population. These calibration variables serve as mediators to adjust and realign the data, enabling accurate population estimates even when the subject data lacks a well-defined relationship with the target population. The calibration variables allow the system to correct selection biases and improve measurement precision without requiring complete census data.
Solution Approach 2:
The patent applies parameter changes by adjusting weighting factors and calibration parameters to transform the subject data set into a representation that mirrors the target population. By changing the parameters of the data (such as applying differential weighting schemes), the system can correct for selection biases and improve the accuracy of population estimates while maintaining the efficiency of non-probability sampling.
2Measurement precision
If personal information and calibration variables are required in the subject data set, then measurement precision improves, but data privacy risks and collection complexity increase
Solution Approach 1:
The patent extracts and separates the calibration variables from the personally identifiable information. The system can obtain calibration variables from external sources or aggregate data without requiring access to individual personal information. This extraction allows the system to maintain measurement precision while reducing data privacy risks, as the calibration process can be performed on aggregated or anonymized data rather than raw personal data.
Solution Approach 2:
The patent creates a simplified copy or representation of the necessary calibration information that does not require actual personal data. By using proxy variables, aggregated statistics, or synthesized calibration data, the system can achieve the same measurement precision without handling sensitive personal information, thus reducing privacy risks while maintaining analytical accuracy.
3Measurement precision
If a complete census is performed on a large target population, then measurement precision improves, but cost and time requirements become prohibitively high
Solution Approach 1:
The patent applies partial action by using a subset of the population (non-probability sample) combined with calibration variables to achieve population estimates that would otherwise require a complete census. Instead of collecting data from every member of the target population, the system uses a manageable subset and adjusts it through calibration, significantly reducing the quantity of data collection required while maintaining high measurement precision.
Solution Approach 2:
The calibration variables act as intermediaries that enable accurate population estimates without requiring complete census data. These intermediaries allow the system to bridge the gap between partial sampling and complete population characterization, providing a cost-effective approach that achieves census-level accuracy without the full census burden.
Data Source
AI summary
Methods and systems are provided herein for calibrating subject data based on reference data, so that the calibrated subject data more closely represents a target population. The methods and systems include partitioning a reference data set into a plurality of reference data partitions using a data partitioning scheme, each reference data partition associated with a characteristic; and partitioning a subject data set into a plurality of subject data partitions using the data partitioning scheme, each subject data partition associated with a characteristic that corresponds to the characteristic associated with a reference data partition of the plurality of reference data partitions; identifying a variable present in the reference data set that is not present in the subject data set; and calculating a value of the variable for each reference data partition based on a rate of occurrence of the variable in each reference data partition.