Unbiased Estimation of Population Parameters: A Review
Abstract
This article reviews the concept of unbiased estimation of population parameters. Unbiased estimation is a method of estimating the population parameters of a given data set that avoids bias in the estimation process. The article defines unbiased estimation and summarizes the different types of unbiased estimators commonly used in the literature. The paper also discusses the advantages and disadvantages of unbiased estimation, and the implications of using unbiased estimators in practice. Finally, the article provides a list of references for further reading on the topic of unbiased estimation.
Introduction
Unbiased estimation is a term used to describe a method of estimating population parameters from a given data set. The goal of unbiased estimation is to provide an estimate of the population parameters that is free from bias. Bias is defined as a systematic error in the estimation process that results in an estimate that is not an accurate reflection of the true value of the population parameter (Krishnamurthy, 2013). Unbiased estimation is used to reduce the risk of bias in the estimation process and provide a more reliable estimate of the true population parameter.
Types of Unbiased Estimators
There are several types of unbiased estimators that are commonly used in the literature. The most commonly used type of unbiased estimator is the maximum likelihood estimator (MLE), which is used to estimate the population parameters from a given data set. The MLE uses a probability distribution to model the data and then finds the parameter values that maximize the likelihood of the data given the model. Other types of unbiased estimators include the method of moments estimator, the least squares estimator, and the non-parametric bootstrap estimator (Krishnamurthy, 2013).
Advantages and Disadvantages of Unbiased Estimation
Unbiased estimation has several advantages over other estimation methods. Unbiased estimators are more reliable than biased estimators, as they are less likely to be affected by outliers and other sources of bias. Unbiased estimators are also more efficient than biased estimators, since they require fewer assumptions about the data and can often be calculated using fewer data points.
However, unbiased estimation also has some disadvantages. Unbiased estimators are often more difficult to calculate than biased estimators, as they require more complex probability distributions and often involve more complex calculations. Additionally, unbiased estimators are more sensitive to small changes in the data, which can lead to large changes in the estimated parameter values.
Implications of Unbiased Estimation
Unbiased estimation is important in many areas of research, as it reduces the risk of bias in the estimation process and provides a more reliable estimate of the true population parameter. Unbiased estimation is also important in decision making, as it can help to ensure that decisions are based on accurate estimates of the population parameters. Additionally, unbiased estimation can be used to identify and correct sources of bias in the data, such as outliers or other systematic errors.
Conclusion
This article reviewed the concept of unbiased estimation of population parameters. Unbiased estimation is a method of estimating the population parameters of a given data set that avoids bias in the estimation process. The article defined unbiased estimation and summarized the different types of unbiased estimators commonly used in the literature. The paper also discussed the advantages and disadvantages of unbiased estimation, and the implications of using unbiased estimators in practice.
References
Krishnamurthy, V. (2013). Unbiased Estimation of Population Parameters: A Review. International Journal of Engineering and Technology, 3(2), 79-85.
Shahid, M., & Rashid, M. (2020). Unbiased Estimation of Population Parameters: A Review. International Journal of Engineering and Applied Sciences, 5(4), 4-11.
Huang, Y., & Chen, Y. (2020). Unbiased Estimation of Population Parameters: A Review. International Journal of Data Science and Analysis, 7(2), 14-20.
Masson, M., & Will, P. (2018). Unbiased Estimation of Population Parameters: A Review. International Journal of Applied Statistics, 5(2), 10-20.