Lecture 6 - Permutation, randomisation and Monte Carlo Tests Flashcards

1
Q

What are disadvantages of Wilcoxon sign test

A

Requires us to formulate H0 in terms of the median

Less powerful than a t-test when parametric assumptions are made

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2
Q

What are advantages and of permutation tests?

A

Advantages:

  • Widely applicable
  • Produces exact results
  • No specific distribution assumed for the data
  • Does not require analytic distribution of test statistic
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3
Q

What are disadvantages of permutation tests?

A
  • Each permutation of the data must be equally likely (or probability of occurence is known)
  • To be generally applicable, requires thought, and ability to program a computer
  • Can be prohibitively computer intensive in complex situations or for large sample sizes.
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4
Q

What are randomization methods?

A

They are like permutation methods, except only a random subset of all possible permutations are generated.

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5
Q

How do you generate one randomization?

A

List the set of values and randomly assign a sign to each value.

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6
Q

What are advantages of randomization tests?

A
  • All of the same advantages as permutations plus
  • much easier to code
  • Number of randomizations is fixed rather than being a function of sample size.
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7
Q

What are disadvantages of randomization tests?

A
  • As with permutation tests, each randomization of the date must be equally likely.
  • Results will vary from run to run
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8
Q

What are Monte Carlo Tests?

A

A generalisation of randomization tests to include parametric distributions for the data.

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9
Q

What is the algorithm for Monte Carlo tests?

A
  1. Set up some distribution for the data, f( ), and some H0.
  2. Repeat the following two steps many times:
    a) simulate a data set according the h0
    b) calculate T(X) using the simulated data
  3. Add T(x) evaluated from the data
  4. Order all the T(X)s
  5. p-value is the proportion of the T(X)s as extreme or more extreme then the one from the sample data.
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10
Q

What are advantages of Monte carlo tests?

A
  • Data can be assumed to follow any distribution - parametric or nonparametric.
  • The analytic distribution of the test statistic is not required.
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11
Q

Disadvantages of Monte Carlo tests?

A
  • For parametric simulations, need to estimate nuisance parameters
  • Results will vary from run to run.
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