Does higher variance in predictions result in higher variance error estimation?

Question

Motivation

Everyone knows that fitting high variance models requires more data. A "yes" answer to the question would suggest that more data is also needed to evaluate these models.

Assumptions

1. $\forall i \neq j, Y_i \perp Y_j$. This is just a usual independence assumption.
2. $\forall i, j, Y_i \perp \hat{Y}_j$. This should come from the fact that outcomes (in our evaluation or "test" dataset) are independent of data used to fit a model which supplies predictions.

Feel free to assume a bit more if necessary, e.g., we're fitting a Gaussian linear model. Keep in mind that because this problem is about prediction, it's unreasonable to assume $\text{E}(Y_i) = \text{E}(Y_j)$ or $\text{E}(\hat{Y}_i) = \text{E}(\hat{Y}_j)$ for $i \neq j$.

Hypothesis

Variance in predictions contributes to variance in error, as error is a function of predictions.

Progress

Math

Is there a way to decompose, for example, $\text{Var}\Big( \frac{1}{n} \sum_{i=1}^{n} (Y_i - \hat{Y}_i)^2 \Big)$, into a sum including $\text{Var}(\hat{Y}_i)$? So far I've broken down this down into a bunch of granular terms, and am trying to consolidate these into something clearer. For notation sake, let $\epsilon_i = Y_i - \hat{Y_i}$.

$$ \begin{equation} \text{Var}\Big( \frac{1}{n} \sum_{i=1}^{n} \epsilon_i^2 \Big) = \frac{1}{n^2} \Big( \sum_{i=1}^{n} \text{Var}(\epsilon_i^2) + 2\sum_{i < j} \text{Cov}(\epsilon_i^2, \epsilon_j^2) \Big). \end{equation} $$

Breaking down $\text{Var}(\epsilon_i^2)$:

$$ \begin{align} \text{Var}(\epsilon_i^2) &= \text{Var}(Y_i^2 - 2Y_i\hat{Y}_i + \hat{Y}_i^2) \\ &= \text{Var}(Y_i^2) + 4\text{Var}(Y_i\hat{Y}_i) + \text{Var}(\hat{Y}_i^2) - 2\text{Cov}(Y_i^2, Y_i\hat{Y}_i) - 2\text{Cov}(\hat{Y}_i^2, Y_i\hat{Y}_i) \\ &= \text{Var}(Y_i^2) + \text{E}(\hat{Y}_i)^2 \text{Var}(Y_i) + \text{Var}(\hat{Y}_i)\text{E}(Y_i)^2 + \text{Var}(\hat{Y}_i^2) \\ &\qquad - 2\text{E}(\hat{Y}_i)\text{Cov}(Y_i^2, Y_i) - 2\text{E}(Y_i)\text{Cov}(\hat{Y}_i^2, \hat{Y}_i). \end{align} $$

Note that I used this fact to break down $\text{Var}(Y_i\hat{Y}_i)$ and this fact to break down the two covariance terms.

Breaking down the covariances $\text{Cov}(\epsilon_i^2, \epsilon_j^2)$ where $i < j$, thanks to @gunes' answer (which I think used the above referenced fact about covariance):

$$ \begin{align} \text{Cov}(\epsilon_i^2, \epsilon_j^2) &= \text{Cov}(Y_i^2 - 2Y_i\hat{Y}_i + \hat{Y}_i^2, Y_j^2 - 2Y_j\hat{Y}_j + \hat{Y}_j^2) \\ &= \text{Cov}(Y_i^2, Y_j^2) - 2\text{Cov}(Y_i^2, Y_j\hat{Y}_j) + \text{Cov}(Y_i^2, \hat{Y}_j^2) \\ &\qquad - 2\text{Cov}(Y_i\hat{Y}_i, Y_j^2) + 4\text{Cov}(Y_i\hat{Y}_i, Y_j\hat{Y}_j) - 2\text{Cov}(Y_i\hat{Y}_i, \hat{Y}_j^2)\\ &\qquad + \text{Cov}(\hat{Y}_i^2, Y_j^2) - 2\text{Cov}(\hat{Y}_i^2, Y_j\hat{Y}_j) + \text{Cov}(\hat{Y}_i^2, \hat{Y}_j^2) \\ &= 0 -2(0) + 0 \\ &\qquad - 2(0) + 4\text{Cov}(Y_i\hat{Y}_i, Y_j\hat{Y}_j) - 2\text{Cov}(Y_i\hat{Y}_i, \hat{Y}_j^2) \\ &\qquad + 0 - 2\text{Cov}(\hat{Y}_i^2, Y_j\hat{Y}_j) + \text{Cov}(\hat{Y}_i^2, \hat{Y}_j^2) \\ &= 4\text{E}(Y_i)\text{E}(Y_j)\text{Cov}(\hat{Y}_i, \hat{Y}_j) - 2\text{E}(Y_i)\text{Cov}(\hat{Y}_i, \hat{Y}_j) \\ &\qquad - 2\text{E}(Y_j)\text{Cov}(\hat{Y}_i, \hat{Y}_j) + \text{Cov}(\hat{Y}_i^2, \hat{Y}_j^2) \\ &= \text{Cov}(\hat{Y}_i, \hat{Y}_j)\big( 4\text{E}(Y_i)\text{E}(Y_j) - 2(\text{E}(Y_i) + \text{E}(Y_j)) \big) + \text{Cov}(\hat{Y}_i^2, \hat{Y}_j^2). \end{align} $$

At this point, employing the assumption that we're fitting a Gaussian linear model may help.

Simulation - LASSO

See this Python notebook.

Problems with this simulation:

1. I thought the variance in predictions decreases as the $l_1$ penalization coefficient (alpha in scikit-learn) increases. This simulation does not have this property.
2. The plot is quite sensitive to dataset parameters. I can explore this more. But overall, the simulation doesn't provide consistent evidence for or against the hypothesis that higher variance in predictions causes higher variance in error.

Simulation - polynomial regression

Maybe there's a weird problem with the way I estimated the total variance in predictions. To avoid estimating it I decided to directly compute it, which is straightforward for unregularized linear regression.

Note that a problem with this simulation is that it currently isn't capable of fitting significantly variant models. Fitting a polynomial regression past degree=6 screws up compute_var_preds, as it needs to compute an inverse. Perhaps there are better numerical approximations to this quantity.

Speaking of compute_var_preds, below is the math for it (the $*$ subscript denotes test data):

$$ \begin{align} \text{Var}(\hat{\mathbf{y}}_*) &= \text{Var}(X_* \hat{\mathbf{\beta}}) \\ &= \text{Var}(X_* (X^T X)^{-1} X^T \mathbf{y}) \\ &= X_* (X^T X)^{-1} X^T \text{Var}(\mathbf{y}) X (X^T X)^{-1} X_*^T \\ &= X_* (X^T X)^{-1} X^T (\sigma^2 I) X (X^T X)^{-1} X_*^T \\ &= \sigma^2 X_* (X^T X)^{-1} X_*^T. \end{align} $$

The total variance of predictions is defined as the sum of elements in $\text{Var}(\hat{\mathbf{y}}_*)$, which is equivalent to (only b/c it simplifies the code):

$$ \begin{align} \sum_{i=1}^{n_*} \text{Var}(\hat{\mathbf{y}}_*)_i &= \sum_{i=1}^{n_*} \sigma^2 \mathbf{x}_{*i}^T (X^T X)^{-1} \mathbf{x}_{*i} \\ &= \text{trace}( \sigma^2 X_* (X^T X)^{-1} X_*^T ). \end{align} $$

# input arguments to simulate simple linear regression
NUM_SAMPLES = 100
NUM_EXAMPLES = 500
TRAIN_FRAC = 0.5
ERROR_SD = 2
X_LB = 0; X_UB = 10
INTERCEPT = 0; SLOPE = 1
POLYNOMIAL_DEGREES = 1:6
# my compute_var_preds code can't handle degrees > 6
# b/c it directly computes an inverse
SEED = 42
fixed data and parameters
ERROR_VAR = ERROR_SD^2
x = runif(NUM_EXAMPLES, min=X_LB, max=X_UB)
beta = c(INTERCEPT, SLOPE)
y_true_mean = cbind(1, x) %*% beta
compute_var_preds = function(X_tr, X_te, error_var=ERROR_VAR) {
Returns sum of true variances of test set predictions using
an analytical formula
X_tr = as.matrix(X_tr)
  X_te = as.matrix(X_te)
  XtX_inv = solve(t(X_tr) %% X_tr)
  true_var_preds = ERROR_VARdiag((X_te %% XtX_inv %% t(X_te)))
  return(sum(true_var_preds))
}
loop takes 5-10 seconds
mses = matrix(rep(NA, NUM_SAMPLESlength(POLYNOMIAL_DEGREES)),
              nrow=NUM_SAMPLES,
              ncol=length(POLYNOMIAL_DEGREES))
var_preds = matrix(rep(NA, NUM_SAMPLESlength(POLYNOMIAL_DEGREES)),
                   nrow=NUM_SAMPLES,
                   ncol=length(POLYNOMIAL_DEGREES))
for (i in 1:NUM_SAMPLES) {
  j = 1 # indexes degree
  for (degree in POLYNOMIAL_DEGREES) {
    # x - polynomial transform, handle degree=0
    if (degree == 0) {
      X_poly = data.frame(x=rep(1, length(x)))
    } else {
      X_poly = data.frame(poly(x, degree=degree, raw=TRUE))
    }
    # y - randomly generate
    set.seed(SEEDij)
    error = rnorm(NUM_EXAMPLES, mean=0, sd=ERROR_SD)
    y = y_true_mean + error
    # split into train and test
    tr_inds = sample(NUM_EXAMPLES, floor(NUM_EXAMPLES*TRAIN_FRAC))
    y_tr = y[tr_inds]
    y_te = y[-tr_inds]
    X_tr = X_poly[tr_inds,,drop=FALSE] # keep the df's row names
    X_te = X_poly[-tr_inds,,drop=FALSE]
    # fit, predict
    model = lm(y_tr ~ ., data=X_tr)
    # suppress warnings about predictions from rank-deficient fit.
    # should only occur for degree 0, but whatever...
    y_tr_pred = suppressWarnings(predict(model, X_tr, type="response"))
    y_te_pred = suppressWarnings(predict(model, X_te, type="response"))
    # store results
    var_preds[i,j] = compute_var_preds(X_tr, X_te)
    mses[i,j] = mean((y_te - y_te_pred)^2)
    j = j+1
  }
}
var_preds_mean = apply(var_preds, MARGIN=2,
                       FUN=mean) # randomness in splits. aggregate w/ mean
var_mses_estimates = apply(mses, MARGIN=2, FUN=var)
final plot of interest
poly_degree_col = 'red'
plot(var_preds_mean, var_mses_estimates,
     xlab='true total Var(predictions)',
     ylab='estimated Var(test MSE)',
     pch=16, cex=1)
text(var_preds_mean, var_mses_estimates,
     POLYNOMIAL_DEGREES, pos=2, col=poly_degree_col)
legend('bottomleft',
       legend='polynomial degree', text.col=poly_degree_col)

Note that y's range is around 0-10.

# misc plots
boxplot(mses,
        names=POLYNOMIAL_DEGREES,
        xlab='polynomial degree',
        ylab='test MSE')
visual check for degree of overfitting for the last degree
x_tr = x[tr_inds]
plot(x_tr, y_tr,
xlab='x (train)',
ylab='y (train)',
main=paste('polynomial degree:', degree))
x_tr_order = order(x_tr) # to plot preds as a line, need x to be sorted
lines(x_tr[x_tr_order],
y_tr_pred[x_tr_order])

IMO, test MSE isn't different enough to draw any solid conclusions about the question. I'm just sharing my progress so far.

Answer 1

For the cross term (which should be $i\neq j$, and multiplied by $2$)

Assume $i\neq j$: $$\begin{align}\operatorname{Cov}(\epsilon_i^2, \epsilon_j^2)&=\operatorname{Cov}(Y_i^2-2Y_i\hat Y_i+\hat Y_i^2, Y_j^2-2Y_j\hat Y_j+\hat Y_j^2)\\&=\operatorname{Cov}(-2Y_i\hat Y_i,-2Y_j\hat Y_j)+\operatorname{Cov}(-2Y_i\hat Y_i, \hat Y_j^2)\\&+ \operatorname{Cov}(\hat Y_i^2, -2Y_j\hat Y_j^2)+\operatorname{Cov}(\hat Y_i^2, \hat Y_j^2)\end{align}$$

First term: $$t_1=4\operatorname{Cov}(Y_i\hat Y_i, Y_j\hat Y_j)=\operatorname{E}[Y_i]\operatorname{E}[Y_j]\operatorname{Cov}(\hat Y_i, \hat Y_j)=\operatorname{E}[Y]^2\operatorname{Cov}(\hat Y_i, \hat Y_j)$$

Second term (and third term, as they are symmetric):

$$t_{2,3}=-2\operatorname{E}[Y]\operatorname{Cov}(\hat Y_i, \hat Y_j^2)$$

And, the fourth term is self-explanatory. I've assumed $Y_i$ come from the same distribution.

I wonder if something like this is what you're seeking for.