2007, Vol 8, No 1
http://hdl.handle.net/123456789/3760
2019-04-22T19:57:06ZOn solutions of the difference equation x_{n+1}=(((-1)?x_{n-4})/(1+(-1)?x_{n}x_{n-1}x_{n-2}x_{n-3}x_{n-4}))
http://hdl.handle.net/123456789/3768
On solutions of the difference equation x_{n+1}=(((-1)?x_{n-4})/(1+(-1)?x_{n}x_{n-1}x_{n-2}x_{n-3}x_{n-4}))
Karataş, Ramazan
http://sjam.selcuk.edu.tr/sjam/article/view/183
2007-01-01T00:00:00ZOn the recursive sequence x_{n+1}=((x_{n-11})/(1+x_{n-1}x_{n-3}x_{n-5}x_{n-7}x_{n-9}))
http://hdl.handle.net/123456789/3767
On the recursive sequence x_{n+1}=((x_{n-11})/(1+x_{n-1}x_{n-3}x_{n-5}x_{n-7}x_{n-9}))
Şimşek, Dağıstan
http://sjam.selcuk.edu.tr/sjam/article/view/180
2007-01-01T00:00:00ZSolution of linear and nonlinear heat equations by differential transform method
http://hdl.handle.net/123456789/3766
Solution of linear and nonlinear heat equations by differential transform method
Ayaz, Fatma; Kangalgil, Figen
The differential transform method is one of the approximate methods which can be easily applied to many linear and nonlinear problems and is capable of reducing the size of computational work. Exact solutions can also be achieved by the known forms of the series solutions. In this paper, we present the definition and operation of the two-dimensional differential transform and investigate the particular exact solutions of linear and nonlinear heat equations that usually arise in mathematical biology by two-dimensional differential transform method. The results of the present method are compared very well with those obtained by decomposition method.
http://sjam.selcuk.edu.tr/sjam/article/view/185
2007-01-08T00:00:00ZA comparative study of fixed effects models and random intercept/slope models as a special case of linear mixed models for repeated measurements
http://hdl.handle.net/123456789/3765
A comparative study of fixed effects models and random intercept/slope models as a special case of linear mixed models for repeated measurements
İyit, Neslihan; Genç, Aşır
Any dataset in which subjects are measured repeatedly over time or space can be described as repeated measurements data. A linear mixed model (LMM) is a powerful method for analyzing repeated measurements data. It is made up of two components. The first component consists of a regression model for the average response over time and the effects of covariates on this average response. The second component provides a model for the pattern of covariances or correlations between the repeated measurements. In this study, a comparative evaluation of fixed effects models with random intercept models and random intercept and slope models as a special case of random effects models from linear mixed models are taken into consideration and the superiority of random intercept and slope models allow to modeling possible heterogeneity in intercepts and in slopes of the individual's own regression line for repeated measurements data is emphasized.
http://sjam.selcuk.edu.tr/sjam/article/view/184
2007-01-01T00:00:00Z