In this paper, we study the estimation and variable selection of the sufficient dimension reduction space for survival data via a new combination of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$L_1$$</EquationSource> </InlineEquation> penalty and the refined outer product of gradient method (rOPG; Xia et al. in J R Stat Soc Ser B 64:363–410, <CitationRef CitationID="CR28">2002</CitationRef>), called SH-OPG...</citationref></equationsource></inlineequation>

While optimal rates of convergence in L <Subscript>2</Subscript> for spectral regularization estimators in statistical inverse problems have been much studied, the pointwise asymptotics for these estimators have received very little consideration. Here, we briefly discuss asymptotic expressions for bias and variance...</subscript>

The validation of causal relationship between two groups of multivariate time series data often requires the precedence knowledge of all variables. However, in practice one finds that some variables may be negligible in describing the underlying causal structure. In this article we provide an...

We consider the problem of identifying multiple outliers in a general class of beta regression models proposed by Ferrari and Cribari-Neto (J Appl Stat 31:799–815, <CitationRef CitationID="CR8">2004</CitationRef>). The currently available single-case deletion diagnostic measures, e.g., the standardized weighted residual (SWR), the...</citationref>

When sample sizes are unequal, problems of heteroscedasticity of the variables given by the absolute deviation from the median arise. This paper studies how the best known heteroscedastic alternatives to the ANOVA F test perform when they are applied to these variables. This procedure leads to...

Many simple and complex methods have been developed to solve the classification problem. Boosting is one of the best known techniques for improving the accuracy of classifiers. However, boosting is prone to overfitting with noisy data and the final model is difficult to interpret. Some boosting...

As biotechnology has made remarkable progress nowadays, there has also been a great improvement on data collection with lower cost and higher quality outcomes. More often than not investigators can obtain the measurements of many disease-related features simultaneously. When multiple potential...

We develop a new <Emphasis FontCategory="SansSerif">R package that computes the probability density function, the hazard rate function, the integrated hazard rate function, and the quantile function for forty four survival models commonly used in actuarial science. A real data application of the package is illustrated. It is...</emphasis>

The stress-strength reliability <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$R=P(YX)$$</EquationSource> </InlineEquation>, where <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$$X$$</EquationSource> </InlineEquation> and <InlineEquation ID="IEq6"> <EquationSource Format="TEX">$$Y$$</EquationSource> </InlineEquation> are independent continuous random variables, has obtained wide attention in many areas of application, such as in engineering statistics and biostatistics. Classical likelihood-based inference about <InlineEquation ID="IEq7"> <EquationSource Format="TEX">$$R$$</EquationSource> </InlineEquation> has been widely...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$k$$</EquationSource> </InlineEquation> nearest neighbors method and...</equationsource></inlineequation>