Abstract The mean residual life function (mrlf) of a subject is defined as the average residual lifetime of the subject given that the subject has survived up to a given time point. A smooth nonparametric estimator of the mrlf is proposed using a scale mixtures of the empirical estimate of the mrlf. Asymptotic properties are established. The performances of the proposed estimator are studied ba...

sensor networks generally consist of a very great number of sensor nodes which will be spread into a vast environment and aggregate data out of it. the sensor nodes are afflicted with some limitations as follows memory, reception, communication as well as calculation capability, and battery power. the transmission of a great amount of extra data increases data transmission and proportionally in...

Abstract: The popularity of Wireless Sensor Networks (WSN) has increased tremendously in recent time due to growth in MicroElectro-Mechanical Systems (MEMS) & wireless communication technology. The main purpose of such networks is to collect information from the environments and deliver the same to the applications to determine characteristics of the environment or detect an event. As sensor no...

In recent years, we have studied information properties of various types of mixtures of probability distributions and introduced a new type, which includes previously known mixtures as special cases. These studies are disseminated in different fields: reliability engineering, econometrics, operations research, probability, the information theory, and data mining. This paper presents a holistic ...

Abstract The mean residual life function (mrlf) of a subject is defined as the expected remaining lifetime of the subject given that the subject has survived up to a given time. The commonly used regression models as proportional mean residual life (PMRL) and linear mean residual life (LMRL) have limited applications due to adhoc restriction on the parameter space. The regression model we propo...

The analyzing and modeling real lifetime data are crucial in many applied sciences including medicine, engineering, insurance and finance, amongst others. The two important one parameter lifetime distributions namely exponential and Lindley [2] are popular for modeling lifetime data from biomedical science and engineering. Recently, Shanker et al. [3] have conducted a comparative and critical s...