Concentration inequalities for estimated least squares regression coefficients? I would like to know what is the best concentration inequality we can use for the estimated least squares regression coefficients. Let $\hat \beta_0, \hat \beta_1$ be the estimated regression coefficients when we solve the following simple linear regression model with ordinary least squares:
$$
Y_i = \beta_0 + \beta_1 X_i + \varepsilon_i, \quad \quad i=1,2,\dots,n,
$$
where $E[\varepsilon_i|X] = 0$ and $\text{Var}[\varepsilon_i|X] = \sigma^2$.
Now consider $\hat \beta_1$ for example, Chebyshev's inequality gives us
$$
P(|\hat \beta_1 - \beta| > t) \le \frac{\text{Var}(\hat \beta_1)}{t^2}.
$$
Is this the only concentration inequality that we can use for $\hat \beta_1$?
I was thinking that maybe we can exploit the fact that $\hat \beta_1$ is asymptotically normal, that is,
$$
\beta_1 \stackrel{a}{\sim} \mathcal{N}\bigg(\beta_1,\frac{\sigma^2}{n} (X^TX)^{-1}\bigg).
$$
Can we use this fact to state a concentration inequality that is tighter than Chebyshev's inequality in the case of a large number of samples?
 A: If $\hat \beta_1$ is approximately normal with mean $\beta_1$, then $Z=|\hat \beta_1-\beta_1|$ is a Half Normal, that has distribution function
$$F_{HN}(z) = 2\Phi(z/\sigma_z) - 1$$
where $\sigma_z$ is the standard deviation of $\hat \beta_1-\beta_1$, and $\Phi$ is the standard Normal distribution function.
So
$${\rm Pr}(|\hat \beta_1-\beta_1| > t) = 1 - F_{HN}(t) = 2 \Phi\left(-\frac{t}{\sigma_z} \right).$$
This gives you the "exact" probability (given the Normal approximation), which in general should have a value below the Chebychev bound.
Does it?
A: Depending on values of $X$ and the moments of $Y$ you can use higher moments in  the same way as the second moment (if you pretend various moments are known  rather than estimated, as you are doing with the variance)
$$\hat\beta = (X^TX)^{-1}\sum_{i=1}^n x_iy_i= n(X^TX)^{-1} \frac{1}{n}\sum_{i=1}^n x_iy_i$$
Using Markov's inequality on the $m$th power of the summands,
$$P(|\frac{1}{n}\sum_{i=1}^n x_iy_i- \frac{1}{n}\sum_{i=1}^n x_iE[y_i]|>t)\leq \frac{\kappa_m}{t^m}$$
where $\kappa_m$ is the $m$th central moment of $\frac{1}{n}\sum_{i=1}^n x_iy_i$, if that moment exists.
And if $\kappa_{\psi_1}=E[\exp( \frac{1}{n}\sum_{i=1}^n x_iy_i   )]$ exists,
$$P(|\frac{1}{n}\sum_{i=1}^n x_iy_i- \frac{1}{n}\sum_{i=1}^n x_iE[y_i]|>t)\leq \frac{\kappa_{\psi_1}}{e^t}$$
The problem with these, as I hinted above, is that the bounds involve the unknown moments, just as Chebyshev's inequality (the $m=2$ case) does. If you're happy with unknown constants in the bounds, that's fine.  The bounds do not, in general, hold when estimates are plugged in for the constants.
For example, take Chebyshev's inequality. To get a bound when $\sigma^2$ is estimated, you need a bound for $\hat\sigma^2-\sigma^2$, and that's the same problem you started with, only harder.  If you know a bound on the fourth moments, you can get a good bound on $\hat\sigma^2-\sigma^2$ -- but we've upgraded from second-moment assumptions to fourth-moment assumptions.
For another example, take the Normal bounds. As we all know, even if $Y$ is exactly Normal with constant (but unknown) variance, we end up with $t$-distributions in the bounds rather than Normals, and it gets worse if $Y$ is non-Normal.
The bounds with estimated constants get closer to holding as the sample size increases (for fixed distributions), but they don't (in general) hold for any finite $n$.
