Computer Intensive Statistical Methods

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Information about Computer Intensive Statistical Methods

Published on March 18, 2014

Author: DanieleBaker



A little summary on several types of computer intensive statistical methods developed from the fantastic book by Malcolm Haddon titled "Modeling and Quantitative Methods in Fisheries"
Authors: Daniele Baker and Stephanie Johnson

Computer Intensive Models Daniele and Steph

Playing Card Example • 52 cards are population ▫ 25 card sample…. What is the mean, median, SE • Randomization ▫ Randomly reallocate cards to groups (i.e. diamonds, spades..) ▫ 4 people divide up cards ▫ Do 1000+ times randomly allocating to the 4 groups ▫ Graph up distribution of parameter ▫ If your original parameter is outside of the 95% CI then it is significantly different from random

Playing Card Example • JackKnife ▫ Take 24 cards… (leave 1 out) and complete test statistics ▫ Redo for all possible combinations (25 x) • Bootstrapping ▫ Pick out 25 cards, but put them back each time ▫ Calculate new parameters  Redo 1000+ times ▫ If your sample parameter falls within the 95% CI of the distribution, then it isn’t statistically different from random

Playing Card Example • Monte Carlo ▫ Find a model that would fit the card trend  Relative frequency plot, examine shape ▫ Randomly select value for model parameters or data (cards picked)  Complete 1000+ times  Analyze your parameters to fit the data vs. random generated parameters

COMPARISON Randomize Jackknife Bootstrap Monte Carlo With Replacement No No Yes Yes Exact P-values Yes Likely no Yes Yes Resample a theoretical PDF Parametric Resample an empirical distribution Yes Yes Non- parametric Non- parametric Good to… Deal with unknown distribution Detect bias, calc. SE, good for biases parameters Calc. sample size for exp. Design, CI, SE and Test Hypot. Flexible, generic, SE, CI, Test Hypot. Good for sparse data sets Limitations Can’t calc SE, or CI (weak) Bad CI

4 Methods • Randomization ▫ Ho: each group of obs. is a random sample of 1 pop. ▫ Must be characterized by a test stat ▫ Combine all groups, then reallocate, and compare  Repeat 1000+ times ▫ Compare obs. Test stat with empirical distribution of that test stat given available data.  A sig diff is when obs. test stat is beyond empirical distribution

4 Methods • Jackknife ▫ Sample could be from one arm of the distribution ▫ Subset data (for all combinations of all data minus 1 pt) (total = n-1) ▫ Calculate pseudo-values , diff. btw. this and obs = estimate of bias ▫ Good when estimating something other than mean ▫ Calculate Jackknife SE and parameter of interest  CI can be fitted but issues with DFs

4 Methods • Bootstrapping ▫ Random samples of the observation (with replacement  Each treated as a separate random sample ▫ Should = the distribution if you had repeatedly sampled the original population ▫ Provides better CI than the Jackknife  Can determine SE, CI and test hypotheses ▫ Have been used for multiple regression and stratified sampling

4 Methods • Monte Carlo ▫ Mathematical model of the situation + model parameters ▫ Randomly select variable, parameter or data values and then use to determine model output  Do 1000+ times and use to test hypotheses or determine Confidence Intervals ▫ *Resampling from a theoretical distribution ▫ Compare observations with data from a model of the system + for use in risk assessment

Chapter 5: Randomization TestsChapter 5: Randomization Tests • ANOVA (e.g.) vs. Randomization ▫ Assumptions • Hypothesis testing ▫ Determinations of likelihood that observations in nature could have arisen by chance ▫ Relativity: group 1 vs. group 2, hypothesis

Standard Significance Testing • Test statistic (e.g. t-test) • Significant difference • Statistically 3 things needed to test hypothesis: 1. Formally stated hypothesis (Ho and Ha) 2. Test statistic: t-test, F-ratio, r correlation, etc. 3. Means of generating PD of test statistic under assumption Ho is true •

• Observed vs. expected values (d.f., etc.) • Determine how likely observed values, assuming Ho to be true • α-value: 0.05, 0.01, 0.001 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 X2 Statistic Probability α 0.05 = ~15

However… • Test statistics are not valid if assumptions are falsely made • Can, therefore, reject a real difference in data or accept a non-existent difference • Problem with theoretically derived PDF: ▫ If test statistic is not significant, cannot tell without further analyses if the test failed b/c: 1. Samples are not independently distributed (thing being tested) or, 2. The data failed to conform to the assumptions necessary for the validity of the test (e.g. samples were not normally distributed) This is where randomization tests come into play…

Significance Testing by Randomization Independent of any determined parametric PDF Generates empirical PDF for the test-statistic

Given a null hypothesis, the expected PDF for a test statistic can be generated by repeatedly randomizing the data with respect to sample group membership and recalculating the test statistic 1. Done many times (min. 1000) 2. Test statistic values tabulated 3. Compared with original value from un- randomized data 4. If original value is unusual relative to permutations, Ho can be rejected

The Three R’s

Key Points • Essentially, the null-hypothesis is that the groups being compared are random samples from the same population • Thus, a test of significance is an attempt to determine whether observed samples could have been randomly drawn from the same population • Answer = probability ▫ PROBABILITY = possibly from same pop. (never claim definitively) ▫ PROBABILITY = not likely from same pop.

Ex: Fish length for in-shore fish vs. off- shore fish • Ha: In-shore fish are smaller on average than off- shore fish 0 50 100 150 200 250 300 Fork Length Off-shore In-shore

Ex. 5.2 • Randomization can be used to test mean difference • Original mean difference occurred 25 x’s out of 1000 • What does this tell us about the data? • Weight of evidence, not significant difference (e.g. p=0.05)

0 100 200 300 400 500 600 700 800 900 1000 0 5 10 15 20 25 30 35 40 45 50 55 Abs(Mean Difference) SortedRandomization Replicates p=0.025

Selection of a Test Statistic • Chosen based on sensitivity to the hypothetical property being checked or compared (e.g. mean vs. median (ex.5.3)) • Precaution should be taken when selecting a non-standard test statistic • Multivariate comparisons • Determine exactly which hypothesis is being tested by the test statistic ▫ “When in doubt, be conservative” • Ex 5.3

Ideal Test Statistics • Greatest statistical power • Significance – probability of making a Type I error • Power – probability of making a Type II error • Unbiased – using a test that is more likely to reject a false hypothesis than a true one No difference Null true Difference exists Null False Null accepted OK TYPE II ERROR Null rejected TYPE I ERROR OK

Randomization of Structured Data • Restricted to comparison tests (cannot be used for parameter estimation) • Differences in variation – randomize residuals instead of data values • Basic rule: unbalanced and highly non-normal data a randomization procedure should be used • Question: what should be randomized? ▫ Raw data, ▫ Sub-set of raw data, or ▫ residuals from model

Take Home Message… With structured or non-linear data, care needs to be taken in what components should be randomized

Summary • Randomization requires less assumptions than standard parametric stats • Significance tests test whether the observed samples could be from the same pop. • State hypotheses and determine significance level • Test statistics that yield the greatest power should be utilized

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