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Non linear model: A model which is non-linear in the parameters, for instance are Some such type of models can be converted into the linear models by linearization (the second equation above, for instance, by taking logarithms throughout). Those which cannot are often referred to as the intrinsically non-linear, though these can often be approximated by the linear equations in some circumstances. Parameters in such type of models usually have to be estimated using an optimization procedure like the Newton-Raphson technique. In such models linear parameters are those for which second partial derivative of the model function with respect to parameter is zero (β1 and β3 in the first example given above); when this is not case (β2 and β4 in the ?rst example above) they are called as non-linear parameters.
This is the theorem which states that if the error terms in a multiple regression have the same variance and are not corrected, then the estimators of the parameters in the model p
historigrams and histogram
Models which make use of the smoothing techniques such as locally weighted regression to identify and represent the possible non-linear relationships between the explanatory and th
Minimum volume ellipsoid is a term for ellipsoid of the minimum volume which covers some specified proportion of the set of multivariate data. It is commonly used to construct rob
Regression to the mean is the procedure first noted by Sir Francis Galton that 'each peculiarity in man is shared by his kinsmen, but on average to the less degree.' Hence the ten
methods of measuring trend
Incidental parameter problem is a problem which sometimes occurs when the number of parameters increases in the tandem with the number of observations. For instance, models for pa
Regression through the origin : In some of the situations a relationship between the two variables estimated by the regression analysis is expected to pass by the origin because th
Pascal's triangle is an arrangement of numbers described by Pascal in his Traité du Triangle Arithmétique published in the year 1665 as 'The number in each cell is equal to in the
Uncertainty analysis is the process for assessing the variability in the outcome variable that is due to the uncertainty in estimating the values of input parameters. A sensitivit
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