求翻译：数理统计中的参数估计是被广泛研究的一个课题，而最小二乘估计则是参数估计中最为常用的估计。最小二乘法是上世纪初由Gauss创立的，自此一直得到广泛应用。然而，当自变量增多，而且变量间存在复共线性问题时，最小二乘估计表现出相当不稳定。近五十年来，统计学家对它做了很多改进工作，提出了各种改进最小二乘估计(LSE)的方法，比较常见的是有偏估计类，如主成分估计(PCE)、岭估计(RE)、岭型主成分估计(RPCE)等。当变量间存在复共线性问题时，在均方误差的意义下，它们均优于最小二乘估计。论文将研究岭型主成分估计，对它进行改进，并重点研究在均方误差的意义下其优良性。是什么意思？

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The number of parameters in the statistical estimates were the subject of a broad range of research, and the minimum two multiplied by the estimated is estimated parameters are most commonly used in the estimate. The least squares is the beginning of the last century was founded by the gauss, since

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Parameter estimation in statistics is a subject of extensive research, least squares estimation is the most commonly used estimate of the parameter estimation. Least squares is founded by Gauss at the beginning of the last century, has been widely used ever since. However, where the independent vari

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