Response variables in medical sciences are often bounded, e.g. proportions, rates or fractions of incidence of some
disease.

In this work, we are interested to study if some characteristics of the population, e.g. sex and race which can
explain the incidence rate of colorectal cancer cases. To accommodate such responses, we propose a new class of
regression models for bounded response by considering a new distribution in the open unit interval which includes a new
parameter to make a more flexible distribution. The proposal is to obtain compound power normal distribution as a base
distribution with a quantile transformation of another family of distributions with the same support and then is to study
some properties of the new family. In addition, the new family is extended to regression models as an alternative to the
regression model with a unit interval response. We also present inferential procedures based on the Bayesian
methodology, specifically a Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of parameters.
An application to real data to illustrate the use of the new family is considered.

we are interested to study if some characteristics of the population, e.g. sex and race which can
explain the incidence rate of colorectal cancer cases.

To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution.

Response variables in medical sciences are often bounded, e.g. proportions, rates or fractions of incidence of some disease. In this work, we are interested to study if some characteristics of the population, e.g. sex and race which can explain the incidence rate of colorectal cancer cases. To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution. The proposal is to obtain compound power normal distribution as a base distribution with a quantile transformation of another family of distributions with the same support and then is to study some properties of the new family. In addition, the new family is extended to regression models as an alternative to the regression model with a unit interval response. We also present inferential procedures based on the Bayesian methodology, specifically a Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of parameters. An application to real data to illustrate the use of the new family is considered.

To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution.

The proposal is to obtain compound power normal distribution as a base distribution with a quantile transformation of another family of distributions with the same support and then is to study some properties of the new family.

This paper analyzes the causal impact of retirement in China on Body Mass Index (BMI) and weight,
which are a good gauge of the risk for some diseases. Many middle income developing countries are aging
very rapidly and may have to adjust the retirement age to have financially feasible government budgets.
It is important to know and understand any plausible health consequences of raising the retirement age
in developing countries, and which sub-populations within these countries may be most affected. By
using 2011, 2013 and 2015 waves of the China Health and Retirement Longitudinal Study (CH**LS),
our identification strategy uses variation in China’s mandatory retirement age with a fuzzy discontinuity
design to examine an exogenous shock to retirement behavior. Our study finds that retirement will
increase weight and BMI among men. This effect is much larger for men with low education. The channel
may be that men with low education drink more and take less vigorous exercises after they get retired.
Retirement does not affect weight and BMI for women. These effects are robust with different definitions
of retirement, narrow retirement bandwidth for samples as well as dropping samples with rural Hukou.

This paper analyzes the causal impact of retirement in China on Body Mass Index (BMI) and weight,
which are a good gauge of the risk for some diseases. Many middle income developing countries are aging
very rapidly and may have to adjust the retirement age to have financially feasible government budgets.

Many middle income developing countries are aging
very rapidly and may have to adjust the retirement age to have financially feasible government budgets.

using 2011, 2013 and 2015 waves of the China Health and Retirement Longitudinal Study (CH**LS),
our identification strategy uses variation in China’s mandatory retirement age with a fuzzy discontinuity