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，适用于带有对数线性误差的异方差线性模型。我们同时估计均值与方差参数，并在稀疏设定下展示了新的渐近分布性与紧致性性质。我们还表明，在调整参数的适当假设下，随着迭代次数增加，零参数的估计值会收缩。通过模拟实验，我们观察到本文结果的可能推广形式，并将该估计方法应用于电力消耗预测。