Zero-inflated explanatory variables are common in fields such as ecology and finance. In this paper we address the problem of having excess of zero values in some explanatory variables which are subject to multioutcome lasso-regularized variable selection. Briefly, the problem results from the failure of the lasso-type of shrinkage methods to recognize any difference between zero value occurring either in the regression coefficient or in the corresponding value of the explanatory variable. This kind of confounding will obviously increase number of false positives - all non-zero regression coefficients do not necessarily represent real outcome effects. We present here the adaptive LAD-lasso for multiple outcomes which extends the earlier work of multivariate LAD-lasso with adaptive penalization. In addition of well known property of having less biased regression coefficients, we show here how the adaptivity improves also method's ability to recover from influences of excess of zero values measured in continuous covariates.

翻译：在生态和金融等领域,零膨胀的解释性变量很常见。 在本文件中,我们处理一些解释性变量的零值超过零的问题,这些变量须经过多种结果的套套套套套套套套套套套套套式的变量选择。简而言之,问题是由于套套套式收缩方法未能认识到在回归系数或解释性变量的相应值中出现的零值之间的任何差异。这种混搭显然会增加假正数的数量,所有非零回归性系数不一定代表实际结果效果。我们在这里为多种结果提供了适应性LAD-lasso,这些结果延续了多种变换式LAD-laso的早期工作,同时对适应性处罚作了补充。除了已知的偏差性回归系数较少外,我们在这里展示适应性如何提高方法的能力,以便从连续变量测量的零值超值的影响中恢复过来。