estimator

简明释义

英[ˈestɪmeɪtə(r)]美[ˈestəˌmeɪtər]

n. [统计] 估计量；评价者

英英释义

单词用法

同义词

反义词

例句

1.TripAdvisor is pushing the "fees estimator" as a key selling feature...

TripAdvisor已经推出了“fees estimator”(费用估计工具)作为主要的卖点。

2.Meanwhile, the stability property of the estimator is also discussed.

同时也讨论了一个有关估计器稳定性的性质。

3.This allows the estimator and the customer to quickly sift through a large number of items without allocating substantial effort to any of them.

这使得估算者和客户不用投入太多精力就能快速地详查大量的条目。

4.Construx Estimator: This tool is provided free of charge (with a limited license), which is quite a value considering the features available.

Construx Estimator：此工具免费提供(有限许可)，从可用的功能而言，这相当有价值。

5.In order to improve the accuracy of the estimator, oversampling technology is adopted.

为了提高估计器的精度，采用了过采样技术。

6.Then, the phase estimator can further determine the phase of frequency response of OFDM channel.

此后，相位估计机可以进一步对OFDM信道的相频响应进行估计。

7.One kind of heteroscedasticity testing method was proposed through extreme value theory and extreme value index estimator.

应用极值理论，通过极值指数估计量，提出了一种可行的对异方差的检验方法。

8.The contractor used a reliable estimator to calculate the total cost of the renovation project.

承包商使用了一个可靠的估算器来计算翻修项目的总成本。

9.An estimator can help businesses predict their expenses for the upcoming year.

一个估算器可以帮助企业预测即将到来的年度开支。

10.In statistics, an estimator is a rule or formula for calculating an estimate of a population parameter.

在统计学中，估算器是用于计算总体参数估计值的规则或公式。

11.The software includes an estimator that provides real-time cost analysis.

该软件包含一个估算器，提供实时成本分析。

12.Using a statistical estimator, researchers were able to analyze the data more effectively.

利用统计估算器，研究人员能够更有效地分析数据。

作文

In the field of statistics and data analysis, the term estimator refers to a rule or a formula that is used to make inferences about a population based on a sample. An estimator is essentially a statistic that provides an estimate of a parameter, such as the mean or variance, which characterizes the entire population. For example, if we want to know the average height of all students in a school, we can take a sample of students and calculate their average height. The formula we use to calculate this average is an estimator. It is important to understand that an estimator is not the actual value of the parameter; rather, it is a calculated value based on the available data. The concept of estimator is crucial in various fields, including economics, engineering, and social sciences. In these disciplines, researchers often rely on samples to draw conclusions about larger populations. For instance, a political analyst might use an estimator to predict election outcomes based on a survey of a small group of voters. The accuracy of the estimator depends on several factors, including the size of the sample, the method of sampling, and the variability within the population. There are different types of estimators, each with its own characteristics and applications. Some common types include point estimators, which provide a single value estimate of a parameter, and interval estimators, which offer a range of values within which the parameter is expected to lie. Point estimators are often easier to compute and interpret, but they do not provide information about the uncertainty associated with the estimate. On the other hand, interval estimators give a more comprehensive view by indicating the level of confidence in the estimate. One of the most widely used estimators is the sample mean, which serves as an estimate of the population mean. The sample mean is calculated by summing all the values in the sample and dividing by the number of observations. While this estimator is straightforward, it can be influenced by outliers—extreme values that differ significantly from other observations. To mitigate this issue, researchers may use robust estimators that are less sensitive to outliers, ensuring more reliable estimates. Moreover, the performance of an estimator can be evaluated using various criteria, such as bias and variance. A good estimator should ideally be unbiased, meaning that its expected value equals the true parameter value. However, in practice, achieving both low bias and low variance can be challenging. Thus, statisticians must carefully choose the appropriate estimator based on the specific context and goals of their analysis. In conclusion, the role of an estimator in statistics cannot be overstated. It allows researchers to make informed decisions and predictions based on limited data. Understanding how to select and evaluate different estimators is essential for anyone engaged in data analysis. As we continue to navigate an increasingly data-driven world, the ability to effectively use estimators will remain a vital skill across various domains of research and industry.

在统计学和数据分析领域，术语估计量指的是一种规则或公式，用于根据样本对总体进行推断。估计量本质上是一个统计量，它提供了一个参数的估计，例如描述整个总体的均值或方差。例如，如果我们想知道一所学校所有学生的平均身高，我们可以抽取一些学生并计算他们的平均身高。我们用来计算这个平均数的公式就是一个估计量。重要的是要理解，估计量并不是参数的实际值；相反，它是基于可用数据计算出的值。 估计量的概念在经济学、工程学和社会科学等多个领域至关重要。在这些学科中，研究人员通常依靠样本来得出关于更大总体的结论。例如，政治分析师可能会使用估计量根据对小组选民的调查来预测选举结果。估计量的准确性取决于几个因素，包括样本的大小、抽样方法和总体内的变异性。 有不同类型的估计量，每种都有其特征和应用。一些常见类型包括点估计量，提供参数的单一值估计，以及区间估计量，提供一个范围，其中参数预计会落入。点估计量通常更易计算和解释，但它不提供与估计相关的不确定性信息。另一方面，区间估计量通过指示对估计值的置信水平，提供了更全面的视角。 最广泛使用的估计量之一是样本均值，它作为总体均值的估计。样本均值是通过将样本中的所有值相加并除以观察数来计算的。虽然这个估计量很简单，但它可能会受到异常值的影响——与其他观察值显著不同的极端值。为了减轻这个问题，研究人员可能会使用稳健的估计量，使其对异常值的敏感性降低，从而确保更可靠的估计。 此外，可以使用各种标准来评估估计量的性能，例如偏差和方差。一个好的估计量应该理想地是无偏的，这意味着它的期望值等于真实的参数值。然而，在实践中，实现低偏差和低方差可能是具有挑战性的。因此，统计学家必须根据分析的具体上下文和目标仔细选择合适的估计量。 总之，估计量在统计学中的作用不可低估。它使研究人员能够根据有限的数据做出明智的决策和预测。理解如何选择和评估不同的估计量对于任何从事数据分析的人来说都是至关重要的。随着我们继续在一个日益数据驱动的世界中航行，有效使用估计量的能力将在各个研究和行业领域保持重要技能。