#### Filter Results:

- Full text PDF available (80)

#### Publication Year

1998

2017

- This year (8)
- Last 5 years (53)
- Last 10 years (74)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Yanyuan Ma, Marc G. Genton
- 2004

We propose a flexible class of skew-symmetric distributions for which the probability density function has the form of a product of a symmetric density and a skewing function. By constructing an enumerable dense subset of skewing functions on a compact set, we are able to consider a family of distributions, which can capture skewness, heavy tails and… (More)

- Yanyuan Ma, Marc G. Genton
- 1998

In this paper, the problem of the robustness of the sample autocovariance function is addressed. We propose a new autocovariance estimator, based on a highly robust estimator of scale. Its robustness properties are studied by means of the in ̄uence function, and a new concept of temporal breakdown point. As the theoretical variance of the estimator does not… (More)

- Peter Hall, Yanyuan Ma
- 2007

We consider functional measurement error models where the measurement error distribution is estimated non-parametrically.We derive a locally efficient semiparametric estimator but propose not to implement it owing to its numerical complexity. Instead, a plug-in estimator is proposed, where the measurement error distribution is estimated through… (More)

Max-stable processes (de Haan, 1984) have received sustained attention in recent years because of their relevance for studying extreme events in financial, environmental and climate sciences. In a seminal unpublished University of Surrey 1990 technical report, R. L. Smith defined Gaussian max-stable processes, where all margins follow a unit Fréchet… (More)

A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population level in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.

- Yanyuan Ma, Liping Zhu
- Annals of statistics
- 2013

We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model. This conversion allows us to derive an efficient estimator which reaches the… (More)

We consider a class of generalized skew-normal distributions that is useful for selection modeling and robustness analysis and derive a class of semiparametric estimators for the location and scale parameters of the central part of the model. We show that these estimators are consistent and asymptotically normal. We present the semiparametric efficiency… (More)

We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating… (More)

- Yanyuan Ma, Guosheng Yin
- 2010

For randomly censored data, the authors propose a general class of semiparametricmedian residual life models. They incorporate covariates in a generalized linear form while leaving the baseline median residual life function completely unspecified. Despite the non-identifiability of the survival function for a given median residual life function, a simple… (More)

- Yanyuan Ma, Liping Zhu
- International statistical review = Revue…
- 2013

Summarizing the effect of many covariates through a few linear combinations is an effective way of reducing covariate dimension and is the backbone of (sufficient) dimension reduction. Because the replacement of high-dimensional covariates by low-dimensional linear combinations is performed with a minimum assumption on the specific regression form, it… (More)