Stanford University, Spring 2016, STATS 205

## Location Model

• $$X_1,X_2,\dots,X_n$$ denotes random sample following the model $X_i = \theta + e_i,$ random errors $$e_i$$ are independently and identically distributed with a continuous probability density function $$f(t)$$ symmetric around $$0$$.

• Hypothesis testing: $H_0: \theta = 0 \text{ versus } H_A: \theta > 0$

• A null hypothesis is associated with a contradiction to a theory one would like to prove
• An alternative hypothesis is associated with the actual theory one would like to prove

## Sign Test

• To every statistical test such as the sign test, we associate a test statistic
• A test statistic is a function of the data

• The sign test has test statistic: $S = \sum_{i=1}^n \operatorname{sign}(X_i)$ with $$\operatorname{sign}(t) = \begin{cases} -1 & \text{if } t < 0 \\ 0 & \text{if } t = 0 \\ 1 & \text{if } t > 0 \end{cases}$$

## Sign Test

• Looking just at the positive observations: $S^+ = \#_i \{ X_i > 0 \}$
• $$0$$ observations are ignored and sample size is reduced
• If observing a $$-1$$ or $$1$$ for each $$X_i$$ is a coin flip then
• $$S^+$$ follows a binomial distribution with $$n$$ trials and success probability $$1/2$$
• This is the null distribution of the test statistic
• It doesn't depend on the error distribution
• This property is called distribution-free
• Compare null distribution with observed test statistic

## Sign Test Example

• Comparison of ice cream brands A and B
• Theory: Brand A is preferred over B
• Null hypothsis: No difference between A and B
• Alternative: A is preferred over B

• Blindfolded taster gives preference of one ice cream over the other
• Experiment with 12 taster
• 10 tasters prefer brand A over brand B
• 1 taster prefers brand B over A
• 1 no preference
• Pretty convincing evidence in favor of brand A
• But how likely is such a result due to chance if the null hypothesis is true (no preference in brands)?

## Sign Test Example

• For our sign test, the test statistic $$S^+$$ is the number positive signs
• Evaluated on our data the observed test statistic is $$s^+ = 10$$
• We compare the observed test statistic with the binomial distribution with $$n = 11$$ trials and success probabilty $$1/2$$

## Sign Test Example

The $$p$$-value is $$P_{H_0}(S^+ \ge s^+) = 1 âˆ’ F_B(s^+ âˆ’ 1,n,\frac{1}{2})$$

## [1] 0.005859375

## Sign Test Example

Significance level: the probabilty of rejecting the null when it is true

We can reject the null hypothesis at significance level $$\alpha = 0.05$$ in favor of the alternative that Brand A is tastier than Brand B.

## Traditional $$t$$-Test

• The $$t$$-test depends on the population pdf of $$f(t)$$
• Thus it is not distribution free
• The usual test statistic is $t = \frac{\bar{X}}{s/\sqrt{n}}$ here $$\bar{X}$$ is the mean and $$s$$ is the standard deviation of the sample
• Usually $$s$$ is not known and has to be estimated, then if the population is normal $$t$$ has a Student $$t$$-distribution with $$n-1$$ degress of freedom
• And the $$p$$-value $$P_{H_0}(t \ge t_0) = 1 - F_T(t_0,n-1)$$

## Signed-Rank Wilcoxon Test

• In contrast to the $$t$$-test, the sign-rank Wilcoxon test uses only the ranks of the distance from $$0$$ $W = \sum_{i>0}^n \operatorname{sign}(X_i) \operatorname{R} |X_i|$
• No closed-form, iterative algorithms, e.g. psignrank in R
• Our test statistics considers positive items only $W^+ = \sum_{X_i > 0} \operatorname{R} |X_i| = \frac{1}{2} W + \frac{n(n+1)}{4}$
• The distribution is symmetric around $$\frac{n(n+1)}{4}$$

## Signed-Rank Wilcoxon Test

The distribution of test statistic cannot be obtained in closed-form.

Enumerate all possible outcomes for sample size three $$n = 3$$:

## Signed-Rank Wilcoxon Test

The distribution of test statistic cannot be obtained in closed-form.

Enumerate all $$2^n$$ possible outcomes for sample size three $$n = 3$$:

##   sign.1 sign.2 sign.3 SumRankPlus
## 1      0      0      0         NaN
## 2      0      0      1         NaN
## 3      0      1      0         NaN
## 4      0      1      1         NaN
## 5      1      0      0         NaN
## 6      1      0      1         NaN
## 7      1      1      0         NaN
## 8      1      1      1         NaN

## Signed-Rank Wilcoxon Test

SumRankPlus = apply(sign*rank,1,sum)
outcome$SumRankPlus[1] = SumRankPlus[1]; outcome ## sign.1 sign.2 sign.3 SumRankPlus ## 1 0 0 0 0 ## 2 0 0 1 NaN ## 3 0 1 0 NaN ## 4 0 1 1 NaN ## 5 1 0 0 NaN ## 6 1 0 1 NaN ## 7 1 1 0 NaN ## 8 1 1 1 NaN ## Signed-Rank Wilcoxon Test outcome$SumRankPlus[2] = SumRankPlus[2]
outcome
##   sign.1 sign.2 sign.3 SumRankPlus
## 1      0      0      0           0
## 2      0      0      1           3
## 3      0      1      0         NaN
## 4      0      1      1         NaN
## 5      1      0      0         NaN
## 6      1      0      1         NaN
## 7      1      1      0         NaN
## 8      1      1      1         NaN

## Signed-Rank Wilcoxon Test

outcome$SumRankPlus = SumRankPlus; outcome ## sign.1 sign.2 sign.3 SumRankPlus ## 1 0 0 0 0 ## 2 0 0 1 3 ## 3 0 1 0 2 ## 4 0 1 1 5 ## 5 1 0 0 1 ## 6 1 0 1 4 ## 7 1 1 0 3 ## 8 1 1 1 6 table(outcome$SumRankPlus)
##
## 0 1 2 3 4 5 6
## 1 1 1 2 1 1 1

## Signed-Rank Wilcoxon Test

Or through Monte Carlo simulations:

n = 3; nsim = 10000
X = matrix(rnorm(n*nsim),ncol=n)
WplusSim = apply(X,1, function(x) { sum( rank(abs(x)) * (x>0) ) })
table(WplusSim)/nsim
## WplusSim
##      0      1      2      3      4      5      6
## 0.1231 0.1235 0.1223 0.2535 0.1225 0.1270 0.1281

and compare with theoretical result:

##     0     1     2     3     4     5     6
## 0.125 0.125 0.125 0.250 0.125 0.125 0.125

## Signed-Rank Wilcoxon Test Example

• Comparison of social awareness in kids going to school versus home schooling
• Theory: Sending kids to school improves social awareness
• Null hypothsis: No difference between kids going to school and staying at home
• Alternative: Kids going to school have improved social awareness

• Eight pairs of identical twins who are of nursery school age.
• For each pair, one is randomly selected to attend nursery school while the other remains at home.
• At the end of the study period, all 16 children are given the same social awareness test.

## Signed-Rank Wilcoxon Test Example

The statistic is the sum of the ranks of positive items:

school = c(82,69,73,43,58,56,76,65); home = c(63,42,74,37,51,43,80,62)
response = school - home
response
## [1] 19 27 -1  6  7 13 -4  3
rank(abs(response))
## [1] 7 8 1 4 5 6 3 2

## Signed-Rank Wilcoxon Test Example

Calculate ranks of absolute values:

response
## [1] 19 27 -1  6  7 13 -4  3
rank(abs(response))
## [1] 7 8 1 4 5 6 3 2

The statistic is the sum of the ranks of positive items:

## [1] 32

## Signed-Rank Wilcoxon Test Example

and using the cumulative distribution function $$F_{W^+}(w^+-1,n)$$ of our test statistic:

## Signed-Rank Wilcoxon Test Example

We can find the $$p$$-value using $$P_{H_0}(W^+ \ge w^+) = 1 - F_{W^+}(w^+ - 1,n)$$:

pvalue = 1-psignrank(wplus-1,n,lower.tail = TRUE)
pvalue
## [1] 0.02734375

## Signed-Rank Wilcoxon Test Example

Significance level: the probabilty of rejecting the null when it is true

We can reject the null hypothesis at significance level $$\alpha = 0.05$$ in favor of the alternative that sending kids to school improves their social awareness.

## Estimation and Confidence Intervals

• All three tests (sign test, signed-rank Wilcoxon, and $$t$$-test) have an associated estimate and confidence interval for the location parameter $$\theta$$
• Recall the model for our sample $$X_1,X_2,\dots,X_n$$ $X_i = \theta_i + e_i$

• Some definitions:
• order statistics: $$X_{(1)}$$ minimum observation, $$X_{(n)}$$ largest observation
• in increasing order $$X_{(1)} < X_{(2)} < \dots < X_{(n)}$$
• quantiles: equally spaced splitting points of continuous intervals with equal probabalities or sample

## Estimation and Confidence Intervals of Sign Test

Estimator is the median: $\hat{\theta} = \operatorname{median}\{ X_1,X_2,\dots,X_n \}$

Confidence interval $$(1âˆ’\alpha)100\%$$: $\left(X_{(c_1+1)},X_{(nâˆ’c_1)} \right),$ where $$c_1$$ is the $$\frac{\alpha}{2}$$ quantile of the binomial distribution

## Estimation and Confidence Intervals of Sign-Rank Wilcoxon

Hodgesâ€“Lehmann estimator: $\hat{\theta} = \operatorname{median}_{i\le j} \left\{ \frac{X_i+X_j}{2} \right\}$

$$A_{ij} = \frac{(X_i +X_j)}{2}, i \le j$$ are called the Walsh averages

and used to form confidence interval $$(1âˆ’\alpha)100\%$$: $\left(A_{(c_2 +1)},A_{([n(n+1)/2]âˆ’c_2 )} \right),$ $$c_2$$ is the $$\frac{\alpha}{2}$$ quantile of the signed-rank Wilcoxon distribution

## Some Comparisons

• Power of a statistical test:
the probability of rejection the null hypothesis when it is false
• The power of the sign test can be low relative to $$t$$-test
• The power of signed-rank Wilcoxon test is nearly that of the $$t$$-test for normal distributions and generally greater than that of the $$t$$-test for distributions with heavier tails than the normal distribution

## Power Simulation $$\theta = 0$$

n = 30; df = 2; nsims = 10000; mu = 0; collwil = rep(0,nsims); collt = rep(0,nsims)
for(i in 1:nsims) {
x = rt(n,df) + mu
wil = wilcox.test(x)
collwil[i] = wil$p.value ttest = t.test(x) collt[i] = ttest$p.value
}
powwil = rep(0,nsims); powwil[collwil <= .05] = 1; powerwil = sum(powwil)/nsims
powt = rep(0,nsims); powt[collt <= .05] = 1; powert = sum(powt)/nsims
powerwil
## [1] 0.0477
powert
## [1] 0.0365

## Power Simulation $$\theta = 0.5$$

n = 30; df = 2; nsims = 10000; mu = 0.5; collwil = rep(0,nsims); collt = rep(0,nsims)
for(i in 1:nsims) {
x = rt(n,df) + mu
wil = wilcox.test(x)
collwil[i] = wil$p.value ttest = t.test(x) collt[i] = ttest$p.value
}
powwil = rep(0,nsims); powwil[collwil <= .05] = 1; powerwil = sum(powwil)/nsims
powt = rep(0,nsims); powt[collt <= .05] = 1; powert = sum(powt)/nsims
powerwil
## [1] 0.4655
powert
## [1] 0.2914

## Power Simulation $$\theta = 1$$

n = 30; df = 2; nsims = 10000; mu = 1; collwil = rep(0,nsims); collt = rep(0,nsims)
for(i in 1:nsims) {
x = rt(n,df) + mu
wil = wilcox.test(x)
collwil[i] = wil$p.value ttest = t.test(x) collt[i] = ttest$p.value
}
powwil = rep(0,nsims); powwil[collwil <= .05] = 1; powerwil = sum(powwil)/nsims
powt = rep(0,nsims); powt[collt <= .05] = 1; powert = sum(powt)/nsims
powerwil
## [1] 0.9245
powert
## [1] 0.698

## Summary

Assumptions on $$f(t)$$:

• Sign Test: any continuous distribution
• Signed-Rank Test: any symmetric continuous distribution
• $$t$$-test: any normal distribution

• The continuity assumption assures that ties are impossible: With probability one we have $$X_i \ne X_j$$ when $$i \ne j$$
• The continuity assumption is only necessary for exact hypothesis tests not for estimates and confidence intervals

Assumptions on $$e_i,e_2,\dots,e_n$$ and thus $$X_1,X_2,\dots,X_n$$:

• independent and identically distributed