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Asymmetric Laplace Parametrisation The asymmetric Laplace distribution is f (y) = δ τ (1 τ )exp{δρ τ (y µ)} for continuously responses  y  where ρ τ (u) = {τ I (u < 0) }u is the so-called check function in Koenker and Bassett (1978), and µ:  is the the location parameter (−∞ < µ < ) τ :  is the xed skewness parameter (0  < τ < 1) δ :  is the inverse scale parameter ( δ > 0). Scale mixtures of normal representation The asymmetric Laplac e random variable  y  can be represented as follows: y  =  µ + ξw  + σ  w/δz, where ξ  =  12τ τ (1τ )  and σ 2 =  2 τ (1τ )  are two scalars depending on  τ . The random variables  w > 0 and z are independent and have exponential distribution with mean  δ 1 and standa rd normal distribution, respectively. As a result,  y  has the follo wing hierarchi cal struct ure: y  | w N  µ + ξ w, σ 2 δ 1 w  and  w Exp(δ ). Approximating check function The log likelihood of asymmetric Laplace distribution has zero second-order derivative everywhere. To implement INLA, we approximate the check function as follows: ˜ ρ τ,γ (u) =  γ 1 log(cosh(τ γ |u|)) if   u 0 γ 1 log(cosh((1 τ )γ |u|)) if   u < 0, where the parameter  γ > 0 is xed and precision of the approximation increases as  γ  → . Link-function The location parameter is linked to the linear predictor by µ =  η Hyperparameters The prior is dened on inverse scale  δ . Specication  family = laplace Hyperparameter spesication and default values Example Notes None. 1

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Laplace

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Page 1: Laplace

 

Asymmetric Laplace

Parametrisation

The asymmetric Laplace distribution is

f (y) = δτ (1− τ )exp{−δρτ (y − µ)}

for continuously responses y  where ρτ (u) = {τ −I (u < 0)}u is the so-called check function in Koenkerand Bassett (1978), and

µ:   is the the location parameter (−∞ < µ < ∞)

τ :  is the fixed skewness parameter (0  < τ < 1)

δ :  is the inverse scale parameter (δ > 0).

Scale mixtures of normal representation

The asymmetric Laplace random variable y  can be represented as follows:

y =  µ + ξw  + σ 

w/δz,

where ξ  =   1−2τ τ (1−τ )  and σ2 =   2

τ (1−τ )  are two scalars depending on  τ . The random variables w > 0 and z

are independent and have exponential distribution with mean  δ −1 and standard normal distribution,respectively. As a result,  y  has the following hierarchical structure:

y | w ∼ N 

µ + ξw, σ2δ −1w

  and   w ∼ Exp(δ ).

Approximating check function

The log likelihood of asymmetric Laplace distribution has zero second-order derivative everywhere.To implement INLA, we approximate the check function as follows:

ρ̃τ,γ (u) =

  γ −1 log(cosh(τγ |u|)) if   u ≥ 0γ −1 log(cosh((1 − τ )γ |u|)) if   u < 0,

where the parameter  γ > 0 is fixed and precision of the approximation increases as  γ  → ∞.

Link-function

The location parameter is linked to the linear predictor by

µ =  η

Hyperparameters

The prior is defined on inverse scale  δ .

Specification

•  family =  laplace

Hyperparameter spesification and default values

Example

Notes

None.

1

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