Likelihood Ratio Test for Numeric Data Error Detection
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Solution Overview
Problem
Conventional methods are ineffective in detecting errors in numeric data due to the complexity of numeric data entry errors, such as transposition, duplication, omission, and substitution, which are not detectable using existing techniques designed for non-numeric data, and manual checks are impractical for large-scale data processing.
Innovation Solution
A likelihood ratio test is used in conjunction with modified kernel density estimation to classify numeric data as valid or invalid by generating histogram-based approximations of kernel density estimates based on valid and invalid data, and computing a likelihood ratio test statistic to determine the validity of new data entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error detection methods (spell checking, dictionary-based approaches) are used for numeric data, then the detection process is simple, but these methods are ineffective in detecting errors in numeric data
Solution Approach 1:
The patent replaces manual inspection and conventional spell-checking mechanisms with an automated statistical system. The system uses kernel density estimation and likelihood ratio tests to automatically detect numeric data errors, substituting human judgment and simple rule-based systems with a more sophisticated but automated statistical approach that can handle the complexity of numeric error patterns.
Solution Approach 2:
The patent transforms the error detection problem by changing the parameters used for analysis. Instead of using raw numeric values directly, the system applies logarithmic transformation (log ratio of each datum to mean value) and uses kernel density estimation to create probability distributions. This parameter transformation enables the system to effectively detect errors that would be invisible in the original numeric space.
2Productivity
If manual checks of numeric data are performed, then detailed error detection is possible, but the process is infeasible beyond very small scale and time-consuming
Solution Approach 1:
The system enables data to detect its own errors through automated statistical analysis. By training the kernel density estimation models on valid and invalid data patterns, the system creates self-awareness of error types. The likelihood ratio test then allows data points to essentially self-evaluate their validity against the learned patterns, eliminating the need for external manual verification while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces kernel density estimation and likelihood ratio statistics as intermediary tools between the raw numeric data and the error detection decision. These statistical intermediaries transform complex numeric patterns into interpretable probability distributions and test statistics, enabling automated high-throughput analysis that maintains the reliability previously achievable only through manual inspection.
3Reliability
If the range of input data values is constrained (e.g., using drop-down lists), then data entry errors are reduced, but the range of valid input data is significantly limited
Solution Approach 1:
The system provides immediate feedback on the validity of numeric data entries by comparing them against learned patterns from training data. The likelihood ratio test generates real-time validation results that inform users whether their input is likely correct or contains errors, allowing flexible open-ended input while maintaining data accuracy through intelligent feedback mechanisms rather than restrictive constraints.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for detecting data errors. A method generally includes receiving a new value as user input for a data field, generating histogram-based approximations of kernel density estimates generated based on valid data and invalid data associated with the data field, determining a likelihood that the new value is a valid value and a likelihood that the new value is an invalid value, computing a likelihood ratio test statistic based on a ratio of the likelihoods, and classifying the new value as valid or invalid based on comparing the likelihood ratio test statistic to a likelihood ratio test threshold.