We show that Akaike’s Information Criterion (AIC) and its variants are asymptotically efficient in integrated autoregressive processes of infinite order (AR(∞)). This result, together with its stationary counterpart established previously in the literature, ensures that AIC can ultimately...

Consider the quantile regression model Y=Xβ+σϵ where the components of ϵ are i.i.d. errors from the asymmetric Laplace distribution with rth quantile equal to 0, where r∈(0,1) is fixed. Kozumi and Kobayashi (2011) [9] introduced a Gibbs sampler that can be used to explore the intractable...

The increment ratio (IR) statistic was first defined and studied in Surgailis et al. (2007) [19] for estimating the memory parameter either of a stationary or an increment stationary Gaussian process. Here three extensions are proposed in the case of stationary processes. First, a...

Local polynomial regression is widely used for nonparametric regression. However, the efficiency of least squares (LS) based methods is adversely affected by outlying observations and heavy tailed distributions. On the other hand, the least absolute deviation (LAD) estimator is more robust, but...

Spatial unilateral autoregressive model Xk,ℓ=αXk−1,ℓ+βXk,ℓ−1+γXk−1,ℓ−1+εk,ℓ is investigated in the unit root case, that is when the parameters are on the boundary of the domain of stability that forms a tetrahedron with vertices (1,1,−1), (1,−1,1), (−1,1,1) and...

Consider any kind of parameter for a probability distribution and a fixed distribution. We study the subsets of the parameter space constituted by all the parameters of the probabilities in the α-trimming of the fixed distribution. These sets will be referred to as parameter trimmed regions....

This paper proposes an algorithm for matrix minimum-distance projection, with respect to a metric induced from an inner product that is the sum of inner products of column vectors, onto the collection of all matrices with their rows restricted in closed convex sets. This algorithm produces a...

In Huang (2010) [8], a test of conditional independence based on maximal nonlinear conditional correlation is proposed and the asymptotic distribution for the test statistic under conditional independence is established for IID data. In this paper, we derive the asymptotic distribution for the...

In High Dimension, Low Sample Size (HDLSS) data situations, where the dimension d is much larger than the sample size n, principal component analysis (PCA) plays an important role in statistical analysis. Under which conditions does the sample PCA well reflect the population covariance...

Markov basis for statistical model of contingency tables gives a useful tool for performing the conditional test of the model via the Markov chain Monte Carlo method. In this paper, we derive explicit forms of Markov bases for change point models and block diagonal effect models, which are...