Data censoring in statistical analysis occurs when the value of an observation is only partially known, typically because it falls below a detection limit, exceeds a measurement threshold, or is otherwise incomplete within a defined range. In crypto investing, this phenomenon affects market data where, for example, order book depth may be truncated, or historical price feeds might contain gaps due to exchange outages or API limitations.
Mechanism
This mechanism results from practical constraints in data collection or reporting systems. For instance, in an order book, bids below a certain price or offers above another may not be displayed, creating left-censored or right-censored data points. Similarly, transaction histories might lack detail for very small or very large trades.
Methodology
Addressing data censoring involves employing specialized statistical techniques that account for the partial information rather than simply discarding the incomplete data. Methods such as survival analysis or specific imputation models are used to avoid biased estimates in risk modeling, backtesting trading strategies, or calculating accurate metrics. This approach is essential for deriving reliable insights from crypto market data, where complete datasets are often unavailable.
Survival bias distorts mean reversion models by systematically removing failing bonds, creating an artificially stable dataset that underestimates risk.
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