We propose an adaptive ridge (AR) based estimation scheme for a heteroscedastic linear model equipped with log-linear errors. We simultaneously estimate the mean and variance parameters and show new asymptotic distributional and tightness properties in a sparse setting. We also show that estimates for zero parameters shrink with more iterations under suitable assumptions for tuning parameters. We observe possible generalizations of this paper's results through simulations and will apply the estimation method in forecasting electricity consumption.

翻译：我们提出了一种基于自适应岭(AR)的估计方案，用于具有对数线性误差项的异方差线性模型。我们同时估计均值与方差参数，并在稀疏设定下证明了新的渐近分布性和紧致性性质。我们还证明，在调优参数的适当假设下，零参数的估计值会随着迭代次数的增加而收缩。通过模拟实验观察了本文结果的可能推广，并将该估计方法应用于电力消耗预测。