Description
Winsorize finite observations at explicit inclusive numeric cutoffs, retaining original changed indexes; does not estimate population tail cutoffs.
A descriptive statistic depends on what is counted, how observations are weighted, and which reference or denominator is used. Removing observations differs from replacing them at cutoffs, and a ratio of totals differs from a mean of individual ratios. Keep those conventions and measurement units explicit when comparing results.
Winsorize finite observations at explicit inclusive numeric cutoffs, retaining original changed indexes; does not estimate population tail cutoffs.
When to use this tool
Use this tool to winsorize finite observations at explicit inclusive numeric cutoffs, retaining original changed indexes; does not estimate population tail cutoffs.
How to use the tool and how it works
- Enter values, minimum, maximum using the field definitions below.
- Run the operation. Correct input errors before interpreting an output.
- Read result.values, result.changed. Use the ordering, boundary rules and absence conditions described below.
Require maximum at least minimum. Clamp every value to the supplied inclusive cutoffs and return original indexes whose values changed.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| values | Finite observations in the measurement units used by reference or cutoffs. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| minimum | Inclusive lower cutoff or mapped-range endpoint, in the values’ units. | A finite number satisfying the operation’s stated comparisons. |
| maximum | Inclusive upper cutoff or mapped-range endpoint, in the values’ units. | A finite number satisfying the operation’s stated comparisons. |
| Output field | Interpretation |
|---|---|
| result | Object containing the named fields below. |
| result.values | Transformed or retained observations in the ordering described above. |
| result.changed | Original indexes of observations clamped outside the supplied cutoffs. |
{
"input": {
"values": [
0,
2,
9
],
"minimum": 1,
"maximum": 5
},
"output": {
"result": {
"values": [
1,
2,
5
],
"changed": [
0,
2
]
}
}
}Limitations and assumptions
- The cutoffs are supplied rather than estimated from the sample.
- Use consistent units within each measured list and its reference/cutoffs. Ratios carry numerator units divided by denominator units unless those units cancel. Ordinary floating-point arithmetic is used; input validation does not establish statistical suitability.
- Every declared top-level input field is required and unexpected fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
- Numeric outputs must be finite and no larger in absolute magnitude than the safe-integer limit; operations returning exact large integers use decimal text. Positions are zero-based and spans end exclusively unless the operation explicitly states a different convention.
Alternative or Complementary methods
Check the descriptive convention and denominator before comparing results. Use a separate inferential model when you need uncertainty, significance, causal interpretation or population estimates.
References
Similar or alternative tools
- Trim Explicit Cutoffs
Trim observations strictly outside supplied numeric cutoffs, preserving original order and removal provenance; no quantile estimation.
- Winsorize Fixed Count per Tail
Apply fixed-count-per-tail winsorization retaining original observation order with a nonempty retained center; exact equal values use stable numeric order.