corrected some pages

References to "/D/est-unb" were replaced by references to "/D/est-bias".

Author: JoramSoch

P/mle-bias.md

@@ -21,7 +21,7 @@ username: "JoramSoch"
 ---
 
 
-**Theorem:** [Maximum likelihood estimation](/D/mle) can result in [biased estimates](/D/est-unb) of model parameters, i.e. estimates whose long-term expected value is unequal to the quantities they estimate:
+**Theorem:** [Maximum likelihood estimation](/D/mle) can result in [biased estimates](/D/est-bias) of model parameters, i.e. estimates whose long-term expected value is unequal to the quantities they estimate:
 
 $$ \label{eq:aicc-aic}
 \mathrm{E}\left[ \hat{\theta}_\mathrm{MLE} \right] = \mathrm{E}\left[ \operatorname*{arg\,max}_\theta \mathrm{LL}_m(\theta) \right] \neq \theta \; .

P/resvar-bias.md

@@ -46,7 +46,7 @@ $$ \label{eq:mean-mle}
 \bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i
 $$
 
-2) and $\hat{\sigma}^2$ is a [biased estimator](/D/est-unb) of $\sigma^2$
+2) and $\hat{\sigma}^2$ is a [biased estimator](/D/est-bias) of $\sigma^2$
 
 $$ \label{eq:resvar-var}
 \mathrm{E}\left[ \hat{\sigma}^2 \right] \neq \sigma^2 \; ,
@@ -144,4 +144,4 @@ $$ \label{eq:E-resvar-mle-s3}
 \end{split}
 $$
 
-which proves the [bias](/D/est-unb) given by \eqref{eq:resvar-bias}.
+which proves the [bias](/D/est-bias) given by \eqref{eq:resvar-bias}.

P/resvar-biasp.md

@@ -47,7 +47,7 @@ $$ \label{eq:beta-mle}
 \hat{\beta} = (X^\mathrm{T} V^{-1} X)^{-1} X^\mathrm{T} V^{-1} y
 $$
 
-2) and $\hat{\sigma}^2$ is a [biased estimator](/D/est-unb) of $\sigma^2$
+2) and $\hat{\sigma}^2$ is a [biased estimator](/D/est-bias) of $\sigma^2$
 
 $$ \label{eq:resvar-var}
 \mathrm{E}\left[ \hat{\sigma}^2 \right] \neq \sigma^2 \; ,

P/resvar-unb.md

@@ -32,7 +32,7 @@ $$ \label{eq:ug}
 y_i \overset{\text{i.i.d.}}{\sim} \mathcal{N}(\mu, \sigma^2), \quad i = 1,\ldots,n \; .
 $$
 
-An [unbiased estimator](/D/est-unb) of $\sigma^2$ is given by
+An [unbiased estimator](/D/est-bias) of $\sigma^2$ is given by
 
 $$ \label{eq:resvar-unb}
 \hat{\sigma}^2_{\mathrm{unb}} = \frac{1}{n-1} \sum_{i=1}^{n} \left( y_i - \bar{y} \right)^2 \; .
@@ -45,7 +45,7 @@ $$ \label{eq:resvar-mle}
 \hat{\sigma}^2_{\mathrm{MLE}} = \frac{1}{n} \sum_{i=1}^{n} \left( y_i - \bar{y} \right)^2
 $$
 
-is a [biased estimator](/D/est-unb) in the sense that
+is a [biased estimator](/D/est-bias) in the sense that
 
 $$ \label{eq:resvar-bias}
 \mathbb{E}\left[ \hat{\sigma}^2_{\mathrm{MLE}} \right] = \frac{n-1}{n} \sigma^2 \; .
@@ -61,7 +61,7 @@ $$ \label{eq:resvar-bias-adj}
 \end{split}
 $$
 
-such that an [unbiased estimator](/D/est-unb) can be constructed as
+such that an [unbiased estimator](/D/est-bias) can be constructed as
 
 $$ \label{eq:resvar-unb-qed}
 \begin{split}

P/resvar-unbp.md

@@ -27,7 +27,7 @@ $$ \label{eq:mlr}
 y = X\beta + \varepsilon, \; \varepsilon \sim \mathcal{N}(0, \sigma^2 V) \; .
 $$
 
-An [unbiased estimator](/D/est-unb) of $\sigma^2$ is given by
+An [unbiased estimator](/D/est-bias) of $\sigma^2$ is given by
 
 $$ \label{eq:sigma-unb}
 \hat{\sigma}^2 = \frac{1}{n-p} (y-X\hat{\beta})^\mathrm{T} V^{-1} (y-X\hat{\beta})
@@ -46,7 +46,7 @@ $$ \label{eq:resvar-mle}
 \hat{\sigma}^2_{\mathrm{MLE}} = \frac{1}{n} (y-X\hat{\beta})^\mathrm{T} V^{-1} (y-X\hat{\beta})
 $$
 
-is a [biased estimator](/D/est-unb) in the sense that
+is a [biased estimator](/D/est-bias) in the sense that
 
 $$ \label{eq:resvar-bias}
 \mathbb{E}\left[ \hat{\sigma}^2_{\mathrm{MLE}} \right] = \frac{n-p}{n} \sigma^2 \; .
@@ -62,7 +62,7 @@ $$ \label{eq:resvar-bias-adj}
 \end{split}
 $$
 
-such that an [unbiased estimator](/D/est-unb) can be constructed as
+such that an [unbiased estimator](/D/est-bias) can be constructed as
 
 $$ \label{eq:resvar-unb-qed}
 \begin{split}

P/slr-olsmean.md

@@ -48,7 +48,7 @@ $$ \label{eq:slr-ols-mean}
 \end{split}
 $$
 
-which means that the [ordinary least squares solution](/P/slr-ols) produces [unbiased estimators](/D/est-unb).
+which means that the [ordinary least squares solution](/P/slr-ols) produces [unbiased estimators](/D/est-bias).
 
 
 **Proof:** According to the simple linear regression model in \eqref{eq:slr}, the expectation of a single data point is