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scoredrivenmodels.jl's Issues

Add distributions

  • Exponential #74
  • Skellam
  • Student t #88
  • Location scale student t #87
  • Skew t student Not possible in Distributions.jl
  • Cauchy Since it mean and variance are undefined it needs more tests
  • Chi #80
  • Skew Normal Not possible in Distributions.jl
  • Negative Binomial #99
  • Chi squared #78
  • LogitNormal #77
  • Pareto Distribution has non zero score on the parameter x_m, should be better investigated
  • Beta 4 parameters #89

`Fitted` vs. `EstimationStats`

There's a lot of redundancy in Fitted and EstimationStats, so I question the need of both types. I think we could just have EstimationStats within Fitted.

Improve type system

The GAS_Sarima struct should be something like this, it does not require to store the distribution inside.

mutable struct GAS_Sarima{D <: Distribution, T <: AbstractFloat} <: SDM
    ฯ‰::Vector{T}
    A::Dict{Int, Matrix{T}}
    B::Dict{Int, Matrix{T}}
    scaling::T
end

The constructor should be somthing like GAS_Sarima{LogNormal}(....) or GAS_Sarima(LogNormal, ....) that turns into a GAS_Sarima{LogNormal}(....).

Error when calculating Hessian

function update_aux_estimation!(aux_est::AuxEstimation{T}, func::Optim.TwiceDifferentiable,
                                opt_result::Optim.OptimizationResults) where T
                                
    push!(aux_est.loglikelihood, -opt_result.minimum)
    push!(aux_est.psi, opt_result.minimizer)
    push!(aux_est.numerical_hessian, Optim.hessian!(func, opt_result.minimizer))
    push!(aux_est.opt_result, opt_result)
    return
end

should be

function update_aux_estimation!(aux_est::AuxEstimation{T}, func::Optim.TwiceDifferentiable,
                                opt_result::Optim.OptimizationResults) where T
    push!(aux_est.numerical_hessian, Optim.hessian!(func, opt_result.minimizer))
    push!(aux_est.opt_result, opt_result)
    push!(aux_est.loglikelihood, -opt_result.minimum)
    push!(aux_est.psi, opt_result.minimizer)
    return
end

TagBot trigger issue

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`fit_stats` double printing

julia> fit_stats(f)
--------------------------------------------------------
Distribution:                 LogNormal
Number of observations:       400
Number of unknown parameters: 10
Log-likelihood:               -779.7883
AIC:                          1579.5766
BIC:                          1619.4912
--------------------------------------------------------
Parameter      Estimate   Std.Error     t stat   p-value
omega_1          0.0135      0.0285     0.4750    0.6450
omega_2         -2.8408      0.0721   -39.4195    0.0000
A_1_11          -0.0378      0.0056    -6.8093    0.0000
A_2_11           0.0047      0.0034     1.4046    0.1904
A_11_11         -0.0178      0.0046    -3.8592    0.0032
A_12_11          0.0576      0.0052    11.0151    0.0000
B_1_11          -0.4784      0.0511    -9.3544    0.0000
B_2_11           0.4682      0.0496     9.4378    0.0000
B_11_11         -0.3055      0.1092    -2.7990    0.0188
B_12_11          1.3088      0.1148    11.3993    0.0000

--------------------------------------------------------
Distribution:                 LogNormal
Number of observations:       400
Number of unknown parameters: 10
Log-likelihood:               -779.7883
AIC:                          1579.5766
BIC:                          1619.4912
--------------------------------------------------------
Parameter      Estimate   Std.Error     t stat   p-value
omega_1          0.0135      0.0285     0.4750    0.6450
omega_2         -2.8408      0.0721   -39.4195    0.0000
A_1_11          -0.0378      0.0056    -6.8093    0.0000
A_2_11           0.0047      0.0034     1.4046    0.1904
A_11_11         -0.0178      0.0046    -3.8592    0.0032
A_12_11          0.0576      0.0052    11.0151    0.0000
B_1_11          -0.4784      0.0511    -9.3544    0.0000
B_2_11           0.4682      0.0496     9.4378    0.0000
B_11_11         -0.3055      0.1092    -2.7990    0.0188
B_12_11          1.3088      0.1148    11.3993    0.0000

Fix link documentation

Right now the logit link is documented as \\tilde{f} = -\\ln(\\frac{b - a}{f + a} - 1) but it is programmed as \\tilde{f} = \\ln(\\frac{f - a}{b - f}). This documentation should be fixed.

Improve fitting prints

I really don't like how in this line

println("initial point $i of $n_initial_points - Log-likelihood: $(-opt_result.minimum)")

it might look like the log-likelihood is associated with the initial point $i. Rather, it should be clear that it is associated with the (supposed) optimal point converged after starting in initial point $i.

A few possible rewordings would be

"Round $i of $n_initial_points"
"Iteration $i of $n_initial_points"

Etc.

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