The parameters only makes sense for a family of distributions. A location parameter is typically a parameter associated with a translation of the elements of the family. The scale parameter is typically associated with scaling of the elements of the family. For a three parameter family where two of the parameters are associated with location and scale the last parameter is often called a shape parameter, but such shape parameters have different interpretation for different families. If the family is infinitely divisible the shape parameter is normally associated with how many distributions are convoluted.

Peter's answer is very good, but for answering fast to your initial question : "no"

You should refer to some mathematicians around you ; the distributions are a huge universe in the math tools for modellers...

Shape parameter basically is a measure to describe the shape (as a result of skewness and kurtosis) of a distribution. There are distributions, such as Gaussian, exponential etc., that have constant skewness and kurtosis. These distributions do not need shape parameter.

But, distributions, such as Weibull, Laplace, Beta etc., have varying shapes. Therefore, they need explicit shape parameter. 

Moreover, although distributions, such as LogNormal, have varying skewness, they do not need explicit shape parameter, because they are determined by scale and/or location parameter.

There is some confusion about location and scale parameters.

First we have the "natural" parameters such as moments (expected value, variance, third centred moment etc). Second we have the parameters that define the density function of the family of distributions (or the discrete probability function).

Any family of distributions has natural parameters that describe the central tendency, the width and skewness and kurtosis.

A functional parameter of a family of distribution is called a location parameter is a parameter that changes by a transformation adding a constant a to the random variable to a new value = old value + the constant.

A parameter is called a scale parameter is a parameter of the family of distributions that changes by scaling the random variable by a multiplicative constant c into c times the old parameter.

For example, X normally distributed with mue and sigma:

mue is a location parameter and sigma is a scale parameter.

For Y 1= a + X it holds, the expected value of Y1 is mue + a.

For Y2 = c times  X, the standard deviation of Y2 equals c times sigma.

Distributions that have scaling or location parameters with these properties are special.

To describe the distribution of any random variable, the moments above give a rough picture of the distribution. Only a few families of distributions have the same shape throughout (for all functional parameters); the same applies for kurtosis.

Thank you all for your valuable time and information, please provide me any link about describe the family of distribution based on the location, scale and shape parameter. If i consider Transmuted Lindley Distribution used in modeling, how these functional parameters location, scale, shape will work.