Concentration of measures via size-biased couplings

Let Y be a nonnegative random variable with mean??? and finite positive variance ?? ², and let Y ^s , defined on the same space as Y, have the Y size-biased distribution, characterized by $$ EYf(Y)]=\mu E f(Y^s) \quad {\rm for\,all\,functions}\,f\,{\rm for\,which\,these\,expectations\,exist}. $$ Under a variety of conditions on Y and the coupling of Y and Y ^s , including combinations of boundedness and monotonicity, one sided concentration of measure inequalities such as $$ P\left(\frac{Y-\mu}{\sigma} \ge t\right)\le {\rm exp}\left(-\frac{t^2}{2(A+Bt)} \right) \quad {\rm for\,all}\,t\, > 0 $$ hold for some explicit A and B. The theorem is applied to the number of bulbs switched on at the terminal time in the so called lightbulb process of Rao et?al. (Sankhy?? 69:137?C161, 2007).