Struct LargeLosses 

pub struct LargeLosses { /* private fields */ }

Large losses for maximum likelihood fits: values, and optionally a reporting threshold, a censoring flag and a weight per loss.

use prospicio_prob::{LargeLosses, Pareto};

let data = LargeLosses::new(vec![1500.0, 2500.0, 4000.0, 10_000.0])
    .unwrap()
    .censored(vec![false, false, false, true])
    .unwrap();
// α = (uncensored count) / Σ ln(y / t) = 3 / ln(1.5 · 2.5 · 4 · 10).
let fit = Pareto::fit(1000.0, &data, None).unwrap();
assert!((fit.alpha() - 3.0 / 150f64.ln()).abs() < 1e-14);

Implementations

impl LargeLosses

pub fn new(values: Vec<f64>) -> Result<Self>

Losses (finite, positive), uncensored, with weight 1 and no reporting threshold beyond the fitted distribution’s own.

pub fn reporting_thresholds(self, reporting: Vec<f64>) -> Result<Self>

Per-loss reporting thresholds r_i ≤ y_i (finite, non-negative).

pub fn censored(self, censored: Vec<bool>) -> Result<Self>

Per-loss censoring: true where the loss was capped by a policy limit at its recorded value.

pub fn weights(self, weights: Vec<f64>) -> Result<Self>

Per-loss weights (finite, positive).

pub fn values(&self) -> &[f64]

pub fn len(&self) -> usize

pub fn is_empty(&self) -> bool

Trait Implementations

impl Clone for LargeLosses

fn clone(&self) -> LargeLosses

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LargeLosses

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for LargeLosses

fn eq(&self, other: &LargeLosses) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for LargeLosses