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Questions and Answers
What is the purpose of a kernel density estimator (KDE)?
What is the purpose of a kernel density estimator (KDE)?
- To identify outliers in the dataset
- To perform linear regression analysis on the data
- To visualize the probability distribution function (pdf) and cumulative distribution function (CDF) graphics (correct)
- To calculate the mean and standard deviation of the data
What is the Gaussian kernel used for in the kernel density estimator?
What is the Gaussian kernel used for in the kernel density estimator?
- It is used for linear regression analysis
- It is used to identify outliers in the dataset
- It is used as a weight to smoothen the probability distribution function (correct)
- It is used to calculate the mean and standard deviation of the data
What happens to the kernel value as |x – xt| increases in the kernel density estimator?
What happens to the kernel value as |x – xt| increases in the kernel density estimator?
- The kernel value remains constant
- The kernel value becomes unpredictable
- The kernel value increases
- The kernel value decreases (correct)
What does the contribution of a farther sample imply in the kernel density estimator?
What does the contribution of a farther sample imply in the kernel density estimator?
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Which type of kernel is considered as the most popular in the kernel density estimator?
Which type of kernel is considered as the most popular in the kernel density estimator?
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