estimator的音標是["est?m?t?] ，基本翻譯是估算員、估計器，速記技巧可以是利用發(fā)音類似的英文單詞或簡寫來記憶。

Estimator這個詞的英文詞源可以追溯到拉丁語詞根“estimare”，意為“估計，估量”。它的變化形式包括過去式“estimated”，過去分詞“estimated”，現(xiàn)在分詞“estimating”等。

相關單詞：

1. Estimate（n. 估計，v. 估計，估量）：這個詞直接來源于estimator，表示對事物進行估計或估量的行為。

2. Assess（v. 評估）：這個詞的含義與estimator相近，都涉及到對事物的價值或重要性進行評估。

3. Projection（n. 預測）：這個詞的含義是對未來進行估計或預測，與estimator有相似的含義。

4. Valuation（n. 估價）：這個詞的含義是對事物的價值進行估計或評估，與estimator有密切關系。

5. Estimation（n. 估計，估算）：這是一個由estimator派生的名詞形式，表示對數(shù)量、價值等的估計。

以上這些單詞都與estimator有密切關系，都涉及到對事物的價值、數(shù)量、重要性等進行估計或評估的行為。這些單詞在英語中廣泛應用，可以幫助我們更好地理解和使用estimator這個詞。

常用短語：

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

雙語例句：

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.