Table 2:

The notation used in the paper.

Parameter Name	Meaning
$N$	number of cases (sample points)
$d$	number of predictors
$X\in R_{ }^{Nxd}$	a dataset containing $N$ cases, each described by $d$ predictors
$q_i,{var}_i,{se}_i,{ci}_i$	the estimate (its variance, standard error, and confidence interval) computed on $X\in R_{ }^{Nxd}$ by a statistical estimator for the $i^{th}$ predictor variable $\left(foreachi\in \left\{1,...,d\right\}\right)$.
$Q=\left[q_i\right]$, $VAR=\left[{var}_i\right]$, $SE=\left[{se}_i\right]$, $CI=\left[{ci}_i\right]$, for each $i\in \left\{1,...,d\right\}$	The vector of all the estimates (their variance, standard error, and confidence interval) computed over all the predictors in a dataset $X\in R_{ }^{Nxd}$
$m$	number of multiple imputations
$\widehat{X^{\left(j\right)}}\in R_{ }^{Nxd}$	The j-th imputed set
$\widehat{q_i^{\left(j\right)}},\widehat{{var}_i^{\left(j\right)}},\widehat{{se}_i^{\left(j\right)}},\widehat{{ci}_i^{\left(j\right)}}$ for each $i\in \left\{1,...,d\right\}$	the estimate (its variance, standard error, and confidence interval) for the $i^{th}$ predictor $\left(i\in \left\{1,...,d\right\}\right)$ of the the j-th imputed set $\widehat{X^{\left(j\right)}}\in R_{ }^{Nxd}$
$\widehat{Q^{\left(j\right)}}=\left[\widehat{q_i^{\left(j\right)}}\right]$ $\widehat{{VAR}^{\left(j\right)}}=\left[\widehat{{var}_i^{\left(j\right)}}\right]$ $\widehat{{SE}^{\left(j\right)}}=\left[\widehat{{se}_i^{\left(j\right)}}\right]$ $\widehat{{CI}^{\left(j\right)}}=\left[\widehat{{ci}_i^{\left(j\right)}}\right]$ for each $i\in \left\{1,...,d\right\}$	The vector of all the estimates (their variance, standard error, and confidence interval) computed over all the predictors in the the j-th imputed set $\widehat{X^{\left(j\right)}}\in R_{ }^{Nxd}$.
$\widehat{q_i},\widehat{{var}_i},\widehat{{se}_i},\widehat{{ci}_i}$ for each $i\in \left\{1,...,d\right\}$	the pooled estimate (its variance, standard error, and confidence interval) obtained by an MI strategy for the $i^{th}$ predictor variable in $X\in R_{ }^{Nxd}$ by applying Rubin’s rule (Rubin et al 1987).
$\widehat{Q}=\frac{1}{m}{\sum }_{j=1}^m\widehat{Q^{\left(j\right)}}=\left[\widehat{q_i}\right]$, for each $i\in \{1,...,d\}$	The vector of the pooled estimates (one estimate per predictor variable) computed by an MI imputation strategy using $m$ imputations
$\widehat{W}=\frac{1}{m}{\sum }_{j=1}^m\widehat{{VAR}^{\left(j\right)}}\approx W_{\mathrm{\infty }}$ $\widehat{W}=\left[\widehat{W_i}\right]$, for each $i\in \{1,...,d\}$	$\widehat{W}$ is the vector of within imputation variances obtained with $m$ imputations (one within imputation variance per predictor variable). $\widehat{W}$ is an estimate of $W_{\mathrm{\infty }}$, the true within imputation variance when $m\to \mathrm{\infty }$
$\widehat{B}=\frac{1}{m-1}{\sum }_{j=1}^m{\left(\widehat{Q^{\left(j\right)}}-\widehat{Q}\right)}^2\approx B_{\mathrm{\infty }}$ $\widehat{B}=\left[\widehat{B_i}\right]$, for each $i\in \{1,...,d\}$	$\widehat{B}$ is the vector of between imputation variances obtained with $m$ imputations (one within imputation variance per predictor variable). $\widehat{B}$ is an estimate of $B_{\mathrm{\infty }}$, the true between imputation variance when $m\to \mathrm{\infty }$
$\widehat{T}=\widehat{W}+(1+\frac{1}{m})\widehat{B}\approx T_{\mathrm{\infty }}$	$\widehat{T}$ is the total variance that estimates the true total variance, $T_{\mathrm{\infty }}$ when $m\to \mathrm{\infty }$
$A$	The number of amputations of the complete dataset
$\underset{\_}{Q}=\frac{1}{A}{\sum }_{a=1}^A\widehat{Q\left(a\right)}=\left[\underset{\_}{q_i}\right]\approx E\left[\widehat{Q}\right]$ for each $i\in \{1,...,d\}$	The vector with the averages of the MI estimates across all the amputations, that approximates the (vector of) expected values of the MI estimates for each predictor