estimators

简明释义

英[ˈɛstɪmeɪtəz]美[ˈɛstəˌmeɪtərz]

[统计]估计量

评价者（estimator 的复数）

英英释义

单词用法

同义词

反义词

例句

1.This thesis is devoted to the study of multi-variate repeated measures models and comparison of estimators.

本论文致力于研究多变量重复测量模型及估计的比较问题。论文的内容分为三个部分。

2.Many estimators are thus left searching for methods that can yield more accurate results.

因此很多评估人员正在寻找那些能产生更精确结果的方法。

3.The unified optimal and steady-state white noise and quasi-white noise estimators are presented.

提出了统一的最优和稳态白噪声和拟白噪声估值器。

4.The paper introduces generalized trapezoidal fuzzy number to describe risk rate, which adds a degree of confidence of estimators opinion.

引入广义梯形模糊数描述风险等级，增加了评价者的置信度指标。

5.However, these methods are estimated according to sample variance and covariance estimators of returns.

然而，这些方法都是根据样本的变异数或报酬的共变异数估算的估计值。

6.Then we propose an improved spectral processing algorithm based on comprehensive analysis of several commonly used SM estimators.

在分析几种常用的气象回波谱矩估计算法的基础上，提出了一种改进的频域估计算法。

7.In the third part, we give the admissible estimators of regression coefficients in statistical linear model with or without constraints.

第三章是关于回归系数的可容许估计，这包括统计线性模型不受约束和受约束的情形。

8.The construction company uses various estimators to calculate project costs accurately.

这家建筑公司使用各种估算工具来准确计算项目成本。

9.In statistics, estimators are crucial for making inferences about a population.

在统计学中，估计量对做出关于总体的推断至关重要。

10.Our team of estimators provided a detailed report on the expected expenses.

我们的估算师团队提供了一份关于预期开支的详细报告。

11.Different types of estimators can lead to different conclusions in data analysis.

不同类型的估计量可能会导致数据分析中的不同结论。

12.The software includes built-in estimators for time and resource allocation.

该软件包含用于时间和资源分配的内置估算工具。

作文

In the field of statistics and data analysis, the term estimators refers to functions or rules that provide estimates of population parameters based on sample data. Understanding estimators is crucial for anyone working with data, as they help in making informed decisions based on incomplete information. For instance, when conducting a survey, researchers gather a sample of responses to infer the opinions of a larger population. The calculated average from this sample serves as an estimator of the true average opinion of the entire population. This process highlights the importance of estimators in deriving insights from limited data. There are various types of estimators, each with its own strengths and weaknesses. Some common examples include the sample mean, sample variance, and regression coefficients. The sample mean is one of the simplest estimators, calculated by summing all sample values and dividing by the number of observations. It provides a quick estimate of the central tendency of a dataset. However, it can be sensitive to outliers, which may skew the results. On the other hand, more complex estimators like maximum likelihood estimators (MLE) are used in advanced statistical modeling. MLEs are derived by maximizing the likelihood function, which measures how well a statistical model explains the observed data. This approach allows for greater flexibility and accuracy in estimating parameters, especially in cases where data does not follow a normal distribution. However, MLEs can be computationally intensive and require a solid understanding of the underlying statistical theory. Additionally, the concept of bias is essential when discussing estimators. A biased estimator systematically overestimates or underestimates the true parameter value. In contrast, an unbiased estimator produces estimates that are correct on average across many samples. For example, the sample mean is an unbiased estimator of the population mean, while the sample variance can be biased if calculated using the sample size instead of the sample size minus one. The choice of estimators can significantly impact the conclusions drawn from data analysis. Researchers must carefully consider the characteristics of their data and the specific goals of their analysis when selecting appropriate estimators. The effectiveness of an estimator can also be evaluated using criteria such as consistency, efficiency, and sufficiency. A consistent estimator converges to the true parameter value as the sample size increases, while an efficient estimator has the smallest possible variance among all unbiased estimators. Sufficiency refers to whether an estimator captures all the relevant information from the data without losing any crucial details. In conclusion, estimators play a vital role in statistical analysis and decision-making processes. They allow researchers to draw conclusions about entire populations based on limited samples, making them indispensable tools in various fields, including economics, psychology, and epidemiology. As data continues to grow in complexity and volume, the development and refinement of estimators will remain a critical area of focus within the statistical community. Understanding how to choose and apply estimators effectively will empower analysts to make more accurate predictions and informed decisions based on their findings.

在统计学和数据分析领域，术语估计量指的是基于样本数据提供对总体参数的估计的函数或规则。理解估计量对于任何从事数据工作的人来说都是至关重要的，因为它们有助于在不完整的信息基础上做出明智的决策。例如，在进行调查时，研究人员收集一部分回应以推断更大总体的意见。从这个样本计算出的平均值作为整个总体真实平均意见的估计量。这个过程突显了估计量在从有限数据中获得洞察力方面的重要性。 估计量有多种类型，每种都有其自身的优缺点。一些常见的例子包括样本均值、样本方差和回归系数。样本均值是最简单的估计量之一，通过将所有样本值相加并除以观察值的数量来计算。它提供了数据集中心趋势的快速估计。然而，它可能对离群值敏感，这可能扭曲结果。 另一方面，更复杂的估计量如最大似然估计量（MLE）用于高级统计建模。MLE是通过最大化似然函数得出的，该函数衡量统计模型解释观察到的数据的能力。这种方法允许在估计参数时具有更大的灵活性和准确性，尤其是在数据不遵循正态分布的情况下。然而，MLE的计算可能非常复杂，并且需要对基础统计理论有扎实的理解。 此外，偏差的概念在讨论估计量时至关重要。一个有偏的估计量系统性地高估或低估真实参数值。相比之下，一个无偏的估计量在多个样本中产生的估计值在平均上是正确的。例如，样本均值是总体均值的无偏估计量，而如果使用样本大小而不是样本大小减一来计算，样本方差可能是有偏的。 估计量的选择可以显著影响从数据分析中得出的结论。研究人员在选择适当的估计量时必须仔细考虑数据的特征和分析的具体目标。可以使用一致性、效率和充分性等标准来评估估计量的有效性。一致的估计量随着样本大小的增加收敛于真实参数值，而有效的估计量在所有无偏估计量中具有最小的方差。充分性是指一个估计量是否捕捉到数据中的所有相关信息，而没有失去任何关键细节。 总之，估计量在统计分析和决策过程中发挥着至关重要的作用。它们允许研究人员根据有限的样本对整个总体得出结论，使其成为经济学、心理学和流行病学等各个领域不可或缺的工具。随着数据复杂性和体积的持续增长，估计量的发展和完善将仍然是统计界关注的关键领域。理解如何有效选择和应用估计量将使分析师能够根据他们的发现做出更准确的预测和明智的决策。