Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small soybean plant observations can bias in conclusion prediction results on growth. The purpose this study is apply bootstrap resampling technique Gompertz model overcomes residual distribution samples, research data was taken from four varieties spacing treatments, five...

To test the adequacy of a parametric model for the conditional expectation of a binary response variable given an explanatory variable, Kolmogorov–Smirnov and Cramér-von Mises statistics, which are based on a marked empirical process introduced by Stute can be used. Dikta, Kvesic, and Schmidt proposed a model-based resampling scheme for the bootstrap and approximated the critical values of test...

We propose and investigate through Monte Carlo simulations two methods for Bayesian inference for the shape and the scale parameters of a Weibull distribution in a small and highly censored sample setting. The rst method, WLB-SIR, is the Sampling Importance Resampling-adjusted Weighted Likelihood Bootstrap of Newton and Raftery. The second one is a new Bayesian Restoration Maximization (BRM) ne...

_________________________________________________________________ In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future values of a variable after a linear ARIMA model has been fitted to a power transformation of it. The advantages over existing methods for computing prediction intervals of power transformed time series are that the proposed bootstr...

Since the early 1980s, a bewildering array of methods for constructing bootstrap con"dence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap con"dence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods f...

The authors propose a bootstrap procedure which estimates the distribution of an estimating function by resampling its terms using bootstrap techniques. Studentized versions of this so-called estimating function (EF) bootstrap yield methods which are invariant under reparametrizations. This approach often has substantial advantage, both in computation and accuracy, over more traditional bootstr...

A global smoothing procedure is developed using basis function approximations for estimating the parameters of a varying-coefficient model with repeated measurements. Inference procedures based on a resampling subject bootstrap are proposed to construct confidence regions and to perform hypothesis testing. Conditional biases and variances of our estimators and their asymptotic consistency are d...

The bootstrap is typically much less reliable in the context of time-series models with serial correlation of unknown form than it is when regularity conditions for the conventional IID bootstrap, based on resampling, apply. It is therefore useful for practitioners to have available diagnostic techniques capable of evaluating bootstrap performance in specific cases. The techniques suggested in ...

This paper considers the problem of constructing confidence intervals for a single parameter θ in a multiparameter or nonparametric family. Hybrid resampling methods, which “hybridize” the essential features of bootstrap and exact methods, are proposed and developed for both parametric and nonparametric situations. In particular, we apply such methods to construct confidence regions, whose cove...