Erkie Asmare 1 and Andualem Begashaw

Advantages of Nonparametric Methods of Efficiency 
Analysis
Due to their numerous advantages, nonparametric methods of 
efficiency analysis have been constantly applied to the agricultur
-
al sector [3]. As a result, nonparametric method has attracted the 
attention of a number of researchers because of its unique ability 
to measure the efficiency of multiple-input and multiple-output of 
DMUs without assigning prior weight to the input and output [18]. 
The nonparametric approach has the advantage of imposing no a 
priori parametric restrictions on the underlying technology [19]. In 
this context, DEA is an effective non-parametric method for eval-
uating the relative efficiency of the decision-making units, which 
does not need the exact functional form between inputs and out-
puts approach [2-4]. The nonparametric method of efficiency has 
the potential to impose axiomatic properties and estimate the fron-
tier non-parametrically [1].
In addition, this method of efficiency analysis used to overcome 
some disadvantages of the parametric methods of efficiency analy
-
sis. The model measures the efficiency of all DMUs without requir
-
ing prior weights for the inputs and outputs. The concept of their 
model relies on assigning virtual weights to inputs and outputs and 
applies linear programming to ascertain the maximum efficiency of 
the DMU under assessment [18]. Therefore, DEA calculations are 
designed to maximize the relative efficiency score of each unit, sub
-
ject to the constraint that the set of weights obtained in this manner 
for each DMU must also be feasible for all the others included in the 
sample [4]. 
Moreover, nonparametric inference has gained popularity be-
cause of several reasons: 
a. 
The computations are easy to estimate. 
b. 
The data need not be measured quantitatively but could 
be in a qualitative format. 
c. 
The data could also be in an ordinal ranking.
d. 
Does not have as many restrictive assumptions as 
parametric statistical inference.