title without abbreviations: minimum squared error, ordinary least square, minimum mean square, least square, mean squared

I am trying to understand methods for point estimation and their hierarchy in an applied perspective

I understand the concept of least squared error.

I understand that ordinary least squares is the linear category of least squred error.

I understand that mean squared error is the fit of the data (variance + bias squared).

What I don't understand is how minimum mean squared error estimation is related to least squared error estimation.

I would be very grateful for an explanation or a reference where this is explained.

I am trying to understand methods for point estimation and their hierarchy in an applied perspective

I understand the concept of least squared error.

I understand that ordinary least squares is the linear category of least squred error.

I understand that mean squared error is the fit of the data (variance + bias squared).

What I don't understand is how minimum mean squared error estimation is related to least squared error estimation.

I would be very grateful for an explanation or a reference where this is explained.

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