Log-location-scale-log-concave distributions for survival and reliability analysis
نویسندگان
چکیده
منابع مشابه
Log-location-scale-log-concave distributions for survival and reliability analysis
We consider a novel sub-class of log-location-scale models for survival and reliability data formed by restricting the density of the underlying location-scale distribution to be log-concave. These models display a number of attractive properties. We particularly explore the shapes of the hazard functions of these, LLSLC, models. A relatively elegant, if partial, theory of hazard shape arises u...
متن کاملLearning Multivariate Log-concave Distributions
We study the problem of estimating multivariate log-concave probability density functions. We prove the first sample complexity upper bound for learning log-concave densities on Rd, for all d ≥ 1. Prior to our work, no upper bound on the sample complexity of this learning problem was known for the case of d > 3. In more detail, we give an estimator that, for any d ≥ 1 and ǫ > 0, draws Õd ( (1/ǫ...
متن کاملInference and Modeling with Log-concave Distributions
Log-concave distributions are an attractive choice for modeling and inference, for several reasons: The class of log-concave distributions contains most of the commonly used parametric distributions and thus is a rich and flexible nonparametric class of distributions. Further, the MLE exists and can be computed with readily available algorithms. Thus, no tuning parameter, such as a bandwidth, i...
متن کاملClustering with mixtures of log-concave distributions
The EM algorithm is a popular tool for clustering observations via a parametric mixture model. Two disadvantages of this approach are that its success depends on the appropriateness of the assumed parametric model, and that each model requires a different implementation of the EM algorithm based on model-specific theoretical derivations. We show how this algorithm can be extended to work with t...
متن کاملA Universal Generator for Discrete Log-concave Distributions
We give an algorithm that can be used to sample from any discrete log-concave distribution (e.g. the binomial and hypergeometric distributions). It is based on rejection from a discrete dominating distribution that consists of parts of the geometric distribution. The algorithm is uniformly fast for all discrete log-concave distributions and not much slower than algorithms designed for a single ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2015
ISSN: 1935-7524
DOI: 10.1214/15-ejs1089