estimator

estimator的音标是["estɪmətə] ，基本翻译是估算员、估计器，速记技巧可以是利用发音类似的英文单词或简写来记忆。

Estimator这个词的英文词源可以追溯到拉丁语词根“estimare”，意为“估计，估量”。它的变化形式包括过去式“estimated”，过去分词“estimated”，现在分词“estimating”等。

相关单词：

1. Estimate（n. 估计，v. 估计，估量）：这个词直接来源于estimator，表示对事物进行估计或估量的行为。

2. Assess（v. 评估）：这个词的含义与estimator相近，都涉及到对事物的价值或重要性进行评估。

3. Projection（n. 预测）：这个词的含义是对未来进行估计或预测，与estimator有相似的含义。

4. Valuation（n. 估价）：这个词的含义是对事物的价值进行估计或评估，与estimator有密切关系。

5. Estimation（n. 估计，估算）：这是一个由estimator派生的名词形式，表示对数量、价值等的估计。

以上这些单词都与estimator有密切关系，都涉及到对事物的价值、数量、重要性等进行估计或评估的行为。这些单词在英语中广泛应用，可以帮助我们更好地理解和使用estimator这个词。

常用短语：

1. estimator of risk

2. estimator of variance

3. estimator of parameters

4. bootstrapped estimator

5. unbiased estimator

6. maximum likelihood estimator

7. point estimator

双语例句：

1. The mean estimator is a commonly used risk estimator.

2. The sample variance is an estimator of variance.

3. The maximum likelihood estimator is a commonly used method for estimating parameters.

4. The bootstrapped estimator is a reliable method for obtaining unbiased estimators.

5. The point estimator is a method used to estimate the value of a parameter.

6. The bias of the estimator needs to be taken into account when using it in practice.

7. The accuracy of the estimator depends on the sample size and the distribution of the data.

英文小作文：

Title: Estimators in Statistics

Estimators are an essential tool in statistics, helping us to make accurate and reliable predictions and explanations of data. There are many types of estimators, each with its own unique properties and applications. From mean estimators to variance estimators, from maximum likelihood estimators to bootstrapped estimators, they all play an important role in our analysis of data.

In practice, we need to carefully consider the properties of the estimator we are using, such as its accuracy, bias, and efficiency, to ensure that we are making the most appropriate decision for our specific needs. Furthermore, we need to be aware of the limitations of the estimator, such as its sensitivity to sample size and data distribution, to avoid misleading conclusions. By using appropriate estimators and understanding their limitations, we can gain a deeper understanding of the data and make more accurate predictions and decisions.