In many real life fields such as medication, engineering and business, among others, modeling and examine lifetime data are crucial. Numerous lifetime distributions have been used to model lifetime data sets  . The quality of the procedures used in a statistical analysis depends heavily on the assumed probability model or distributions. Because of this, a number of standard probability distributions along with relevant statistical methodologies are presented in literature. But, there still remain several problems where the real data set does not follow any of the classical or standard probability models. In this article we present a new form of the Rayleigh distribution called the area-biased Rayleigh distribution. Rayleigh  derived Rayleigh distribution from the ambit of noise resultant from many vital sources. The Rayleigh distribution has a variety of applications including life testing, reliability analysis, applied statistics and clinical studies. The beginning and other characteristic of this distribution can be found in Siddiqui  , and Hirano  . Howlader  demonstrated the importance of this distribution in communication engineering. Lalitha and Mishra  presented modified maximum likelihood estimation for scaler parameter of Rayleigh distribution. Abd Elfattah et al.  Studied the effect of different methods of sampling schemes on the estimation of parameter for Rayleigh distribution. Further importance of Rayleigh distribution can be observed from Merovci  transmuted Rayleigh distribution, Das and Roy  length biased form of the Weighted Generalized Rayleigh distribution, Hoffman and Karst  properties of the Rayleigh distribution and applications of Rayleigh distribution to the analysis of the responses of marine vehicles to wave excitation.
A random variable X is said to have the Rayleigh distribution (RD) with parameter σ if its probability density function is given by
while the cumulative distribution function of the Rayleigh distribution
where σ denote the scale parameter.
One of the generalized Rayleigh distribution is given by
Pdf in Equation (5) is also named as chi-squared distribution with N degree of freedom and scale parameter σ.
The concept of weighted distributions was initially introduced by Fisher  to the study of effect of methods of ascertainment upon estimation of frequencies. On the other hand, Rao  presented a unified theory of weighted distributions. Rao  identified various real life situations that can be modeled by weighted distributions, where the observations cannot be arise from the original distributions. These situations may occur due to non-observable of some events or damage caused to the original observation ensuing in a reduced value, or arises in practice when observations from a sample are recorded with unequal probabilities.
Weighted distributions had been frequently used in research related to reliability, bio-medicine, meta-analysis, econometrics, survival analysis, renewal processes, physics, ecology and branching processes can be observed in Patil and Ord  , Patil and Rao  , Gupta and Keating  .
Suppose X is a non-negative random variable with its pdf , is a parameter, then distribution is weighted version of , and is defined as
where is an arbitrary non-negative function. For it is called size biased and area biased distributions respectively. The pdf of size-biased Rayleigh distribution is
2. Area Biased Rayleigh Distribution
Using Equation (1) and Equation (6), pdf of the area biased Rayleigh distribution (ARD) is
Rayleigh distribution in (1) and size-biased Rayleigh distribution in (7) are special cases of the generalized Rayleigh distribution in (6) for N = 2 and N = 3 respectively. Moreover, the newly derived area-biased Rayleigh distribution is also a special case of generalized Rayleigh distribution given in (6) for N = 4.
Cumulative distribution function (cdf) of the ARD
is lower incomplete gamma function.
Figure 1. Pdf graph for different values of σ.
Figure 2. Pdf graph for different values of σ.
Moments and Shannon Entropy
The rth moments of the ARD are
For r = 1, 2, 3, 4 in Equation (10), the first four moments of the ARD are
First four mean moments of the ARD are
As the expressions of in Equation (15) are independent of so, applying value of , . So ARD is positively skewed and leptokurtic.
Median of the ARD is
is lower incomplete gamma function.
Mode of the ARD is
The Shannon entropy of the ARD is
3. Estimation of Parameters
In this section parameter of ARD is estimated through method of moments (MOM) and maximum likelihood estimator (MLE).
Equating and Equation (11) as we get MOM estimator of σ
The likelihood function of (8)
Applying natural logarithm as loge
Taking derivative of the Equation (20), we get
Equating (19) to zero and simplifying we get the MLE estimator of σ
Theorem 3.1: If follows the ARD then MOM of σ is unbiased and have minimum variance.
Proof: Applying expectation on (19) and simplifying it we get
So is unbiased estimator of σ. Applying variance on (19), we get
After some simplifications we get,
As . So for large “n”, MOM estimator of σ have minimum variance.
Theorem 3.2: If follows the ARD then MLE of is unbiased and have minimum variance.
Proof: Applying expectation on (22) and simplifying it we get
So is unbiased estimator of and is biased estimator of σ.
Now applying variance on (22), we get
As . So for large “n”, MLE estimator of have minimum variance.
3.3. Cramer Rao Lower Bound
Theorem 3.3: Let
be a random sample from a pdf
in (8), where
Proof: Taking second derivative of (21), we get
Applying expectation on (27) and simplifying it
Substituting (30) and (31) in (28), we get
So the unbiased estimator estimator of attains the Cramer Lower Bound.
4. Bayesian Estimation
The posterior probability distribution function can be derived by using
Using ARD in (8) and uniform prior , in (32) we get the posterior pdf of ARD as
Using (33) the Bayesian estimator of σ
5. Reliability Measures
The survival function of the ARD
is upper incomplete gamma function.
The hazard function of the ALD is
In this section ARD is applied on two life time data sets and compared it with Lindley distribution (LD), Exponential distribution, quasi Lindley distribution (QLD), Rayleigh distribution (RD) and size-biased Rayleigh distribution (SRD) by using Kalmogorov Smirnov (K-S) Statistic. ARD is also compared for survival function and hazard function with RD and SRD on both data 1 and only with SRD for data 2 as RD is not provided good fit for data 2.
Data set 1: This data set represents the lifetime’s data relating to relief times (in minutes) of 20 patients receiving an analgesic and reported by Gross and Clark  : 1.1, 1.4, 1.3, 1.7, 1.9, 1.8, 1.6, 2.2, 1.7, 2.7, 4.1, 1.8, 1.5, 1.2, 1.4, 3, 1.7, 2.3, 1.6, 2.
Data Set 2: This data set is the strength data of glass of the aircraft window reported by Fuller et al.  : 18.83, 20.8, 21.657, 23.03, 23.23, 24.05, 24.321, 25.5, 25.52, 25.8, 26.69, 26.77, 26.78, 27.05, 27.67, 29.9, 31.11, 33.2, 33.73, 33.76, 33.89, 34.76, 35.75, 35.91, 36.98, 37.08, 37.09, 39.58, 44.045, 45.29, 45.381.
Form Table 1, it can be seen that the K-S value for ARD is lower than the other discussed models so ARD is providing better alternate for the above data sets.
1) The survival function graphs of the ARD are smoothly decreasing as compare to RD and SRD.
2) The hazard function graphs of the ARD are monotonically increasing in a smooth way as compare to RD and SRD. From Figure 4 it can be seen that the hazard rate of 20 patients receiving analgesic is monotonically increasing. During an initial period, the risk is low but subsequently increases that may indicate that the patients who are receiving this painkiller drug might be suffering from severe side effects of it. We may conclude that these 20 patients have risk that gradually increases with entire range of life, which may be a result of ineffective treatment.
1) The survival function graphs of the ARD are smoothly decreasing as compare to SRD.
2) The hazard function graphs of the ARD are monotonically increasing; it means that the instantaneous failure for the strength of aircraft window is increasing.
Table 1. Comparison of KS test between distributions Lindley, Exponential, QLD, SRD and ARD.
Figure 3. Survival function graph for relief time (in minutes) of 20 patients.
Figure 4. Hazard function graph for relief time (in minutes) of 20 patients.
In this article a new weighted single parameter Rayleigh distribution named as area-biased Rayleigh distribution (ARD) is introduced. Various properties of the ARD have been derived. It can be seen from Figure 1, Figure 2 and coefficient of skewness and kurtosis that the ARD is positively skewed. Parameter is estimated by the method of MOM, ML and Bayesian. The properties of the MOM and MLE have been proved. It is shown that the estimated parameter by MOM and MLE is unbiased and having minimum variance for large “n”. The ML
Figure 5. Survival graph of strength of glass of the aircraft window.
Figure 6. Hazard graph of strength of glass of the aircraft window.
estimator attains the Cramer Rao lower bound. Then the model is applied into two life time data sets. Kolmogorov Smirnov (K-S) test statistic is used to see the fit good on both data sets. The value of K-S for ARD is compared with some other well-known models named Lindley, Exponential, Quasi Lindley, Size-Biased Rayleigh and it is concluded that ARD is showing better fit on such kind of data sets as comparing to these models. At the end ARD, RD and SRD are used to show the graphical trend of the survival function and hazard function on both data sets. The survival function graph of SRD is decreasing more smoothly as comparing to other models. The hazard function graph of ARD for both data sets is increasing gradually. It means that the instantaneous failures are increasing. Overall it can be seen that ARD model can be a best alternative of the other well-known models and it is showing wide applications in the field of medical.
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