## reserving.TailBondy


The Bondy tail, as chainladder-python's [TailBondy](reserving.TailBondy.md#prospicio.reserving.TailBondy).


Usage


``` python
reserving.TailBondy()
```


Each log factor from [earliest_age](reserving.TailBondy.md#prospicio.reserving.TailBondy.earliest_age) on is taken as [b](pricing.Mbbefd.md#prospicio.pricing.Mbbefd.b) times the one before it, [b](pricing.Mbbefd.md#prospicio.pricing.Mbbefd.b) fitted by least squares. The fitted factors are `f0 ** (b ** j)` from the factor `f0` at [earliest_age](reserving.TailBondy.md#prospicio.reserving.TailBondy.earliest_age), and those past the next one multiply to the last fitted factor raised to `b / (1 - b)`. With the default [earliest_age](reserving.TailBondy.md#prospicio.reserving.TailBondy.earliest_age) (the age of the last factor) [b](pricing.Mbbefd.md#prospicio.pricing.Mbbefd.b) is 1/2 and the tail repeats the last factor.


## Parameters


`earliest_age: int`  
First age in months whose factor enters the fit (the last age at or before it, as chainladder-python reads it); the age of the last factor by default.

`attachment_age: int`  
The factor from this age (the last age at or before it) to the next is kept and the fitted ones replace those after it; the age of the last factor by default. Not before [earliest_age](reserving.TailBondy.md#prospicio.reserving.TailBondy.earliest_age).


## Examples

``` python
>>> from prospicio.reserving import ChainLadder, TailBondy, Triangle
>>> tri = Triangle.from_long(
...     [2020, 2020, 2020, 2021, 2021, 2022],
...     [12, 24, 36, 12, 24, 12],
...     [100.0, 150.0, 165.0, 110.0, 170.0, 120.0],
... )
>>> round(ChainLadder(tail=TailBondy()).fit(tri, "values").tail, 12)
```

1.1


## Attributes

| Name | Description |
|----|----|
| [attachment_age](#attachment_age) | Age after which the fitted factors replace the estimated ones; |
| [earliest_age](#earliest_age) | First age whose factor enters the fit; `None` is the age of the |

------------------------------------------------------------------------


#### attachment_age


Age after which the fitted factors replace the estimated ones;


`attachment_age: int | None`


`None` is the age of the last factor.


------------------------------------------------------------------------


#### earliest_age


First age whose factor enters the fit; `None` is the age of the


`earliest_age: int | None`


last factor.