Question: Suppose that $\boldsymbol{Y} = \boldsymbol{X}\boldsymbol{\beta} + \boldsymbol{\epsilon}$, and the errors have zero mean, and are uncorrelated with constant variance. Let $\hat{\boldsymbol{\beta}}$ be the least squares estimator of $\boldsymbol{\beta}$. Let $\boldsymbol{H} = \boldsymbol{X}(\boldsymbol{X}^T \boldsymbol{X})^{-1}\boldsymbol{X}^T$ be the hat matrix, and $h_{ii}$ be the $i$-th element on its diagonal. Show that $$ \frac{e_i^2}{\| (\boldsymbol{I} - \boldsymbol{H})\boldsymbol{Y} \|^2} \le 1 - h_{ii}$$ where $e_i = Y_i - x_i^T \hat{\boldsymbol{\beta}}$ is the $i$-th residual and $\boldsymbol{I}$ is the $n \times n$ identity matrix, where $n$ is the size of vector $\boldsymbol{Y}$.

Attempt: I got as far as writing the $e_i$ in terms of the matrices above.

$$ e_i = \sum_j (\delta_{ij} - h_{ij})Y_j = Y_i - \sum_j h_{ij}Y_j,$$

where I use $\boldsymbol{e} = (\boldsymbol{I}-\boldsymbol{H})\boldsymbol{Y}$. I square to obtain

$$ e_i^2 = Y_i^2 - 2 Y_i \sum_j h_{ij}Y_j + (\sum_j h_{ij}Y_j)^2,$$

which is then simplified to

$$ e_i^2 = Y_i^2 - 2 Y_i \sum_j h_{ij}Y_j + \sum_j h_{ij}^2 Y_j^2 + \sum_j \sum_{k \neq j} h_{ij} h_{ik} Y_i Y_j.$$

This did not lead me anywhere further. Could anyone please offer me a hint?


2 Answers 2


Note that, from $\mathbf e=(\mathbf I-\mathbf H) \mathbf Y, $ \begin{align}e_i&=-h_{i1}Y_1-h_{i2}Y_2-\cdots+(1-h_{ii})Y_i-\cdots-h_{in}Y_n\\&=\mathbf c^\top\mathbf Y, \tag 1\label 1\end{align} where $\mathbf c:=(-h_{i1}, h_{i2}, \ldots, (1-h_{ii}), \ldots, -h_{in})^\top.$

Using the fact that $\textrm{SS}(\mathbf c^\top\mathbf Y) =\left(\mathbf c^\top\mathbf Y\right)^2/\mathbf c^\top\mathbf c,$ and $\mathbf c^\top\mathbf c$ in $\eqref 1$ being equal to $(1-h_{ii}),$ the sum of squares due to the $i$th residual error component is $$\textrm{SSE}_i=\frac{e_i^2}{(1-h_{ii})}.\tag 2$$

Therefore, the sum of squares for error with $i$th case deleted can be given as $$\textrm{SSE}_{[i]}=\textrm{SSE}-\textrm{SSE}_i; \tag 3\label 3$$ (alternatively, you can write \begin{align}\textrm{SSE}_{[i]}&=\mathbf Y_{[i]}^\top\mathbf Y_{[i]}-\mathbf Y_{[i]}^\top\mathbf X_{[i]}\left(\mathbf X_{[i]}^\top\mathbf X_{[i]}\right) ^{-1}\mathbf X_{[i]}^\top\mathbf Y_{[i]}\\&=\left(\mathbf Y^\top\mathbf Y-y_i^2\right) -\left(\mathbf Y^\top\mathbf X-y_i\mathbf x_i^\top\right) \left\{\hat{\boldsymbol{\beta}}-\left[(\mathbf X^\top \mathbf X)^{-1}\mathbf x_ie_i\right]/(1-h_{ii})\right\};\end{align} simplify it and this would yield $\eqref 3.$)

$\textrm{SSE}= \mathbf e^\top\mathbf e=\|\mathbf e\|^2=\|(\mathbf I-\mathbf H) \mathbf Y\|^2$ and that $\textrm{SSE}_{[i]}\geq 0;$ from $\eqref 3,$ you can conclude your concerned inequality relation.


$\rm [I]$ Applied Regression Analysis, Norman R. Draper, Harry Smith, John Wiley & Sons, $1998, $ pp. $163, ~207-208.$

$\rm [II]$ Plane Answers to Complex Questions: The Theory of Linear Models, Ronald Christensen, Springer Science$+$ Business, $2011,$ sec. $ 13.6,$ pp. $372-373.$


To save some typing, I will not make symbols boldfaced in the following answer.

In essence, this is just a disguise of Cauchy-Schwarz inequality. To wit, let $u_i$ denote the $n$-long column vector whose $i$-th entry is $1$ and all the remaining entries $0$. By definition, the inequality of your interest is then (the key observation is that $1 - h_{ii} = u_i^\top(I - H)u_i$): \begin{align*} \left(u_i^\top(I - H)Y\right)^2 \leq u_i^\top(I - H)u_i \cdot Y^\top(I - H)Y. \tag{1}\label{zx1} \end{align*} Now use the property that $I - H$ is symmetric and idempotent, the expression in the parentheses of the left hand side of $\eqref{zx1}$ is $((I - H)u_i)^\top (I - H)Y$. Hence $\eqref{zx1}$ is equivalent to \begin{align} ((I - H)u_i)^\top(I - H)Y)^2 \leq u_i^\top(I - H)u_i \cdot Y^\top(I - H)Y. \tag{2}\label{zx2} \end{align}

It should be clear now that $\eqref{zx2}$ is the Cauchy-Schwarz inequality (the inner-product space form) $$(a^\top b)^2 \leq \|a\|^2 \|b\|^2$$ with $a = (I - H)u_i$ and $b = (I - H)Y$.

  • 1
    $\begingroup$ It should be fine now. I think the culprit is that I used the same "label" as you to reference it. It is indeed a somewhat strange bug to be fixed by stackexchange (before making the label differ from yours, even the equation cannot be rendered) $\endgroup$
    – Zhanxiong
    Commented Oct 31, 2023 at 4:44

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