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RELEASE NAME....: Pluralsight.com.Implementing.Bootstrap.Methods.in.R-ELOHiM
RELEASE DATE....: 2020-05-10
RELEASE SIZE....: 15x15Mb
FORMAT..........: Bookware
LANGUAGE........: English
URL.............: https://www.pluralsight.com/courses/implementing-bootstrap-methods-r
Perhaps the most common type of problem in statistics involves
estimating some property of the population, and also quantifying
how confident you can be in our estimates of that estimate.
Indeed, the very name of the field, statistics, derives from the
word statistic, which is a property of a sample; using that
statistic you wish to estimate the parameter, which is the same
property for the population as a whole.
Now if the property you wish to estimate is a simple one - say
the mean - and if the population has nice, and known properties -
say it is normally distributed - then this problem is often quite
easy to solve. But what if you wish to estimate a very complex,
arcane property of a population about which you know almost
nothing? In this course, Implementing Bootstrap Methods in R, you
will explore an almost magical technique known as the bootstrap
method, which can be used in exactly such situations.
First, you will learn how the Bootstrap method works and how it
basically relies on collecting one sample from the population,
and then subsequently re-sampling from that sample - exactly as
if that sample were the population itself - but crucially, doing
so with replacement. You will learn how the Bootstrap is a
non-parametric technique that almost seems like cheating, but in
fact, is both theoretically sound as well as practically robust
and easy to implement.
Next, you will discover how different variations of the bootstrap
approach mitigate specific problems that can arise when using
this technique. You will see how the conventional Bootstrap can
be tweaked so that it fits into a Bayesian approach that goes one
step beyond giving us just confidence intervals and actually
yields likelihood estimates. You will also see how the smooth
bootstrap is equivalent to the use of a Kernel Density Estimator
and helps smooth out outliers from the original sample.
Finally, you will explore how regression problems can be solved
using the bootstrap method. You will learn the specific
advantages of the bootstrap - for instance in calculating
confidence intervals around the R-squared, which is something
that is quite difficult to do using conventional parametric
methods. You will explore two variants of the bootstrap method in
the context of regression - case resampling and residual
resampling, and understand the different assumptions underlying
these two approaches.
When youÆre finished with this course, you will have the skills
and knowledge to identify situations where the bootstrap method
can be used to estimate population parameters along with
appropriate confidence intervals, as well as to implement
statistically sound bootstrap algorithms in R.
Level: Advanced
Released: May 01, 2020
Duration: 2h 10m