In　estimating　logistic　regression　models,　convergence　of　the　maximization　algorithm　is　critical;　however,　this　may　fail.　Numerous　bias　correction　methods　for　maximum　likelihood　estimates　of　parameters　have　been　conducted　for　cases　of　complete　data　sets,　and　also　for　longitudinal　models.　Balanced　data　sets　yield　consistent　estimates　from　conditional　logit　estimators　for　binary　response　panel　data　models.　When　faced　with　a　missing　covariates　problem,　researchers　adopt　various　imputation　techniques　to　complete　the　data　and　without　loss　of　generality;　consistent　estimates　still　suffice　asymptotically.　For　maximum　likelihood　estimates　of　the　parameters　for　logistic　regression　in　cases　of　imputed　covariates,　the　optimal　choice　of　an　imputation　technique　that　yields　the　best　estimates　with　minimum　variance　is　still　elusive.　This　paper　aims　to　examine　the　behaviour　of　the　Hessian　matrix　with　optimal　values　of　the　imputed　covariates　vector,　which　will　make　the　Newton-Raphson　algorithm　converge　faster　through　a　reduced　absolute　value　of　the　product　of　the　score　function　and　the　inverse　fisher　information　component.　We　focus　on　a　method　used　to　modify　the　conditional　likelihood　function　through　the　partitioning　of　the　covariate　matrix.　We　also　confirm　that　the　positive　moduli　of　the　Hessian　for　conditional　estimators　are　sufficient　for　the　concavity　of　the　log-likelihood　function,　resulting　in　optimum　parameter　estimates.　An　increased　Hessian　modulus　ensures　the　faster　convergence　of　the　parameter　estimates.　Simulation　results　reveal　that　model-based　imputations　perform　better　than　classical　imputation　techniques,　yielding　estimates　with　smaller　bias　and　higher　precision　for　the　conditional　maximum　likelihood　estimation　of　nonlinear　panel　models.