nonparametric

简明释义

英[ˌnɒnˌpærəˈmetrɪk]美[ˈnɑːnpærəˈmetrɪk]

adj. [数] 非参数的

英英释义

单词用法

同义词

反义词

例句

1.A multimodal nonparametric background model is proposed to detect moving objects by background subtraction.

提出了一种多模态非参数背景模型，用于背景减方法检测运动目标。

2.The performance of nonparametric oi radar clutter density can be improved by using the transformed method.

对雷达杂波密度进行非参数估计时用变换方法可提高估计精度。

3.A novel nonparametric classifier called NNL classifier is introduced to detect double-talk.

一种称为NNL分类器的新的非参数分类器被用来检测双端通话。

4.This paper discusses the parametric and nonparametric production frontiers.

讨论了生产前沿参数方法与非参数方法。

5.The mean shift algorithm is a nonparametric statistical method for seeking the nearest mode of a point sample distribution.

均值移位算法是一种搜索与样本点分布最相近模式的非参数统计方法。

6.Furthermore, the nonparametric form of bootstrapping makes no assumptions about the underlying PDF.

另外，非参数化形式的bootstrapping不需要对底层pdf做任何假设。

7.When analyzing ordinal data, researchers often prefer nonparametric 非参数 statistics.

在分析有序数据时，研究人员通常更喜欢使用非参数 nonparametric 统计。

8.The nonparametric 非参数 approach is often used in machine learning for classification tasks.

非参数 nonparametric 方法通常用于机器学习中的分类任务。

9.In situations where the sample size is small, a nonparametric 非参数 method can provide more reliable results.

在样本量较小的情况下，非参数 nonparametric 方法可以提供更可靠的结果。

10.The researchers employed a nonparametric 非参数 test to analyze the data, as the assumptions of normality were not met.

研究人员使用了一个非参数 nonparametric 检验来分析数据，因为正态性假设未得到满足。

11.A common nonparametric 非参数 test is the Mann-Whitney U test, which compares differences between two independent groups.

一个常见的非参数 nonparametric 检验是曼-惠特尼 U 检验，它比较两个独立组之间的差异。

作文

In the realm of statistics, the term nonparametric refers to a class of methods that do not assume a specific distribution for the data. This is particularly important when dealing with real-world data, which often does not conform to the assumptions required by parametric tests. For instance, when we analyze the heights of individuals in a population, we might expect them to follow a normal distribution. However, this is not always the case. By using nonparametric methods, we can make inferences without the stringent requirements of normality. One of the key advantages of nonparametric techniques is their flexibility. They can be applied to ordinal data or data that do not meet the assumptions of interval data. For example, consider a survey that asks respondents to rank their satisfaction on a scale from 1 to 5. The data collected is ordinal, and applying traditional parametric tests may lead to misleading results. Instead, we can use nonparametric tests such as the Mann-Whitney U test or the Kruskal-Wallis test, which are designed to handle such data appropriately.Moreover, nonparametric methods are robust against outliers. In many datasets, outliers can skew the results significantly when using parametric tests. For instance, if we were analyzing the income levels within a city, a few extremely high incomes could distort the average, leading to an inaccurate representation of the general population's income. Nonparametric methods, on the other hand, often rely on ranks rather than actual values, making them less sensitive to extreme values.Another important aspect of nonparametric statistics is that they can be used for hypothesis testing without the need for large sample sizes. In cases where data is scarce, researchers may struggle to meet the assumptions of parametric tests, which often require larger samples to ensure validity. Nonparametric tests, however, can yield valid results even with smaller datasets, allowing researchers to draw conclusions despite limited data availability.In conclusion, the significance of nonparametric methods in statistical analysis cannot be overstated. They provide a valuable alternative to traditional parametric tests, particularly in situations where data does not meet certain assumptions. Their ability to handle ordinal data, robustness against outliers, and applicability to small sample sizes make them indispensable tools in the statistician's arsenal. As we continue to gather and analyze data from diverse fields, understanding and utilizing nonparametric techniques will be crucial for accurate and meaningful interpretations of our findings.

在统计学领域，术语nonparametric指的是一类不假设数据特定分布的方法。这在处理现实世界数据时尤为重要，因为这些数据通常不符合参数检验所需的假设。例如，当我们分析一个人群的身高时，我们可能会期望它们遵循正态分布。然而，这并非总是如此。通过使用nonparametric方法，我们可以在没有严格的正态性要求的情况下进行推断。Nonparametric技术的一个主要优点是其灵活性。它们可以应用于序数数据或不满足区间数据假设的数据。例如，考虑一项调查，要求受访者在1到5的范围内对他们的满意度进行排名。收集的数据是序数的，应用传统的参数检验可能会导致误导性的结果。相反，我们可以使用nonparametric检验，例如Mann-Whitney U检验或Kruskal-Wallis检验，这些检验旨在适当地处理此类数据。此外，nonparametric方法对异常值具有鲁棒性。在许多数据集中，异常值在使用参数检验时可能会显著扭曲结果。例如，如果我们分析一个城市的收入水平，少数极高的收入可能会扭曲平均值，从而导致对一般人口收入的不准确表述。而nonparametric方法则通常依赖于等级而不是实际值，使其对极端值的敏感度降低。Nonparametric统计的另一个重要方面是它们可以在不需要大样本量的情况下用于假设检验。在数据稀缺的情况下，研究人员可能难以满足参数检验的假设，而这些检验通常需要较大的样本来确保有效性。然而，nonparametric检验即使在小数据集上也能产生有效结果，使研究人员能够在有限的数据可用性下得出结论。总之，nonparametric方法在统计分析中的重要性不容小觑。它们为传统的参数检验提供了有价值的替代方案，特别是在数据不满足某些假设的情况下。它们处理序数数据的能力、对异常值的鲁棒性以及对小样本大小的适用性使它们成为统计学家工具箱中不可或缺的工具。随着我们继续从各个领域收集和分析数据，理解和利用nonparametric技术对于准确和有意义地解释我们的发现将至关重要。