Multiple imputation (MI) inference handles missing data by imputing the values $m$ times, and then combining results from complete-data analyses. However, existing method for likelihood ratio tests (LRTs) has multiple defects: (i) combined test statistic can be negative, but its null distribution is approximated an $F$-distribution; (ii) it not invariant to re-parametrization; (iii) fails ensur...