This study offers a new technique for constructing percentile bootstrap intervals to predict the regression of univariate local polynomials. Bootstrap uses resampling derived from paired and residual methods. The main objective this is perform comparative analysis between two methods by considering nominal coverage probability. Resampling nonparametric with return method, where each sample poin...

Proportional hazards models and, in some situations, accelerated failure time models, are not suitable for analyzing data when the ratio between two individuals is constant. We present a Weibull model with covariables on location and scale parameters. By considering effects of only parameter, but also scale, regression should be able to adequately describe difference treatments. In addition, de...

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression...

Ž . are observed instead of the usual Y , Z , where I for some censori i i Y T 4 i i ing times T . This observation scheme is also called the binary choice model i Ž . Coslett 1987 and Case 1 interval censoring Groeneboom and Wellner Ž . 4 4 1992 . We assume that the errors are independent of Z , T and are i i i Ž . i.i.d. variables with a common distribution F t . Situations involving interval...

Current regression models for interval-valued data do not guarantee that the predicted lower bound of the interval is always smaller than its upper bound. We propose a constrained regression model that preserves the natural order of the interval in all instances, either for in-sample fitted intervals or for interval forecasts. Within the framework of interval time series, we specify a general d...

In some regression problems, it may be more reasonable to predict intervals rather than precise values. We are interested in finding intervals which simultaneously for all input instances x ∈ X contain a β proportion of the response values. We name this problem simultaneous interval regression. This is similar to simultaneous tolerance intervals for regression with a high confidence level γ ≈ 1...