Canadian Journal of Statistics-Revue Canadienne De Statistique最新文献

Several procedures have been recently proposed to test the simplifying assumption for conditional copulas. Instead of considering pointwise conditioning events, we study the constancy of the conditional dependence structure when some covariates belong to general Borel conditioning subsets. We introduce several test statistics based on the equality of conditional Kendall's taus and derive their asymptotic distributions under the null hypothesis. In settings where such conditioning events are not fixed ex ante, we propose a data-driven procedure to recursively build such relevant subsets. This procedure is based on decision trees that maximize the differences between the conditional Kendall's taus, which correspond to the leaves of the trees. Empirical results for such tests are illustrated in the Supplementary Material. Moreover, a study of the conditional dependence between financial stock returns is presented and highlights specific contagion effects of past returns. The last application deals with conditional dependence between coverage amounts in an insurance dataset.

Three-level fractional factorial split-plot (FFSP) designs with the whole plot (WP) factors being more important than the subplot factors are considered in the article. An aliased component-number pattern of type WP (WP-ACNP) is introduced for ranking such designs. The criterion of general minimum lower-order confounding of type WP (WP-GMC) is proposed based on WP-ACNP. The expressions of the key components in WP-ACNP are given via complementary sets. Some necessary conditions for FFSP designs to be WP-GMC FFSP designs are given and some three-level WP-GMC FFSP designs are constructed and tabulated.

This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.

Semiparametric models hold promise to address many challenges to statistical inference that arise from real-world applications, but their novelty and theoretical complexity create challenges for estimation. Taking advantage of the broad applicability of semiparametric models, we propose some novel and improved methods to estimate the regression coefficients of generalized partially linear models (GPLM). This model extends the generalized linear model by adding a nonparametric component. Like in parametric models, variable selection is important in the GPLM to single out the inactive covariates for the response. Instead of deleting inactive covariates, our approach uses them as auxiliary information in the estimation procedure. We then define two models, one that includes all the covariates and another that includes the active covariates only. We then combine these two model estimators optimally to form the pretest and shrinkage estimators. Asymptotic properties are studied to derive the asymptotic biases and risks of the proposed estimators. We show that if the shrinkage dimension exceeds two, the asymptotic risks of the shrinkage estimators are strictly less than those of the full model estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed estimation methods. We then apply our proposed methods to two real data sets. Our simulation and real data results show that the proposed estimators perform with higher accuracy and lower variability in the estimation of regression parameters for GPLM compared with competing estimation methods.

Molecular sequence data are a primary source of information about evolutionary relationships. Over the past few decades, there have been dramatic increases in the sizes of data available. Consequently, focus has shifted towards increasingly complex models that are less prone to the biases that are a consequence of model misspecification. At the same time, the computational challenges, which were always substantial, have become greater due to these increasingly complex models and larger data sizes. In this article, we review phylogenetic inference using sequence data and some recent advances in phylogenetic modelling. We discuss strategies for dealing with complex models, future challenges and paths forward.

The conventional historical functional linear model relates the current value of the functional response at time $��t�$ to all past values of the functional covariate up to time $��t�$. Motivated by situations where it is more reasonable to assume that only recent, instead of all, past values of the functional covariate have an impact on the functional response, in this work we investigate the historical functional linear model with an unknown forward time lag into the history. In addition to estimating the bivariate regression coefficient function, we also aim to identify the historical time lag from the data, which is important in many applications. To this end, we propose an estimation procedure that uses the finite element method to conform naturally to the trapezoidal domain of the bivariate coefficient function. We use a nested group bridge penalty to facilitate simultaneous estimation of the bivariate coefficient function and the historical lag, and show that our proposed estimators are consistent. We demonstrate this method of estimation in a real data example investigating the effect of muscle activation recorded via the noninvasive electromyography (EMG) method on lip acceleration during speech production. In addition, we examine the finite sample performance of our proposed method in comparison with the conventional approach to estimation via simulation studies.

Affiliation networks contain a set of actors and a set of events, where edges denote the affiliation relationships between actors and events. Here, we introduce a class of affiliation network models for modelling the degree heterogeneity, where two sets of degree parameters are used to measure the activeness of actors and the popularity of events, respectively. We develop the moment method to infer these degree parameters. We establish a unified theoretical framework in which the consistency and asymptotic normality of the moment estimator hold as the numbers of actors and events both go to infinity. We apply our results to several popular models with weighted edges, including generalized $��\beta �$-, Poisson and Rayleigh models. Simulation studies and a realistic example that involves the Poisson model provide concrete evidence that supports our theoretical findings.

A new classification method for functional data is proposed in this article. This work is motivated by the need to identify features that discriminate between neurological conditions on which local field potentials (LFPs) were recorded. Regardless of the condition, these LFPs have zero mean, and thus the first moments of these random processes do not have discriminating power. We propose the variation pattern classification (VPC) method which employs the second-moment structure as the discriminating feature and uses the Hilbert–Schmidt norm to measure the discrepancy between the second-moment structure of different groups. The proposed VPC method is demonstrated to be sensitive to the discrepancy, potentially leading to a higher rate of classification. One important innovation lies in the dimension reduction where the VPC method adaptively determines the basis functions (discriminative feature functions) that account for the major discrepancy. In addition, the selected discriminative feature functions provide insights into the discrepancy between different groups because they reveal the features of variation pattern that differentiate groups. Consistency properties are established and, furthermore, simulation studies and the analysis of rat brain LFP trajectories empirically demonstrate the advantages and effectiveness of the proposed method.

In this article, we propose an automatic structure recovery method for generalized additive models (GAMs) by extending Wu and Stefanski's approach. In a similar vein, the proposed method is based on a local scoring algorithm coupled with local polynomial smoothing, along with a kernel-based variable selection approach. Given a specific degree $��M�$, the goal is to identify predictors contributing polynomially at different degrees up to $��M�$ and predictors that contribute beyond degree $��M�$. By focusing on two GAMs, logistic regression and Poisson regression, we illustrate the performance of the proposed method using Monte Carlo simulation studies and two real data examples.

In this article, we focus on the two-sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample sizes. First, we construct a general class of weighted spatial sign tests for the two-sample location problem, which can include some existing high-dimensional nonparametric tests. Then, in this article, we find a locally most powerful test by choosing the inverse norm weight function, named the two-sample inverse norm sign test (tINST). The proposed test can be viewed as an extension of the inverse norm sign test devised for the one-sample problem. We establish the asymptotic properties of the proposed test, which indicate that it is consistent and has greater power than competing tests that belong to the proposed class of weighted spatial sign tests for two-sample location problems. Finally, a large number of numerical investigations and a practical biomedical example demonstrate the power and robustness advantages of the proposed test.