@article{MAKHILLAJ202015520709,
    title = {Statistical Analysis to Mammal Studies Based on Mammal Sleep Data},
    journal = {Agricultural Journal},
    volume = {15},
    number = {5},
    pages = {97-106},
    year = {2020},
    issn = {1816-9155},
    doi = {aj.2020.97.106},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9155&doi=aj.2020.97.106},
    author = {Liming},
    keywords = {ASE,adaptive Lasso,model selection,Mammal sleep,Lasso,SBC (Schwarz Bayesian information),AIC (Akaike Information Criterion)},
    abstract = {The researcher analyzes mammal sleep with 62
species in 1976 by using Lasso method (least absolute
shrinkage and selection operator)that provides stability,
higher selection variables, computational efficiency and
higher prediction accuracy. the results of Average
Parameter Estimate for using adaptive Lasso in SAS
indicates that the position of slow wave and paradoxical
sleep is account for 100%, overall danger index is 93%.
The distributions of overall danger index and slow wave
with paradoxical sleep as wee as gestation time from Refit
model shows normal histogram for paradoxical sleep. In
partition statement of 	“glmselect”procedure, ASE value
(Average Square Error) of the validation from overall
danger index is the minimum of all parameters in the
selected model. On the other hand in selection steps for
ASE, the adaptive Lasso method seems to have fewer
than Lasso; for complicate and large data, elastic net can
deal with more parameters than observations and combine
one and a couple of groups that are consist of multiple
variables by shrinking the coefficients of correlated
variables toward each other.}
    }