Sometimes in practice, data are a function of another variable, and this type of data is called functional data. If the response variable is scalar and is either ordinal or discrete, and the auxiliary variables are functional, then a generalized functional linear model is used to analyze this type of data.
In this paper, a generalized functional linear truncated model is investigated and a maximum likelihood approach is used to obtain estimates of the model parameters. Finally, a simulation study and two practical examples are presented to implement the presented model and methods.
Papi, F., Malekzadeh, P. & Hosseini, ,. F. (2020). Maximum Likelihood Estimation of Parameters in a Generalized Functional Linear Model. Andishe_ye Amari, 24(2), 43-54.
MLA
Papi, F., Malekzadeh, P., & Hosseini,. F. "Maximum Likelihood Estimation of Parameters in a Generalized Functional Linear Model", Andishe_ye Amari, 24, 2, 2020, 43-54.
HARVARD
Papi F., Malekzadeh P., Hosseini ,. F. (2020). 'Maximum Likelihood Estimation of Parameters in a Generalized Functional Linear Model', Andishe_ye Amari, 24(2), pp. 43-54.
CHICAGO
F. Papi, P. Malekzadeh & ,. F. Hosseini, "Maximum Likelihood Estimation of Parameters in a Generalized Functional Linear Model," Andishe_ye Amari, 24 2 (2020): 43-54,
VANCOUVER
Papi F., Malekzadeh P., Hosseini ,. F. Maximum Likelihood Estimation of Parameters in a Generalized Functional Linear Model. Andishe_ye Amari. 2020;24(2):43-54 (In Persian).
