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Estimating weak periodic vector autoregressive time series
This article develops the asymptotic distribution of the least squares estimator of the model parameters in periodic vector autoregressive time...
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On agricultural commodities’ extreme price risk
We show how fat tails in agricultural commodity returns arise endogenously from productivity shocks in a standard macroeconomic model. Using nearly...
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Wigner and Wishart ensembles for sparse Vinberg models
Vinberg cones and the ambient vector spaces are important in modern statistics of sparse models. The aim of this paper is to study eigenvalue...
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Investigating Several Fundamental Properties of Random Lobster Trees and Random Spider Trees
In this paper, we investigate several random structures, namely two classes of random lobster trees (RLTs) and a class of random spider trees (RSTs)....
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Gradient boosting with extreme-value theory for wildfire prediction
This paper details the approach of the team Kohrrelation in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire...
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Asymptotic normality of some conditional nonparametric functional parameters in high-dimensional statistics
This paper deals with the convergence in distribution of estimators of some conditional parameters in the Functional Data Analysis framework. In...
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Nonstationarity and Cointegrations
This chapter deals with some advanced topics of time series analysis. We define the two concepts of stochastic trend and stochastic seasonality,...
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Clustering Longitudinal Data for Growth Curve Modelling by Gibbs Sampler and Information Criterion
Clustering longitudinal data for growth curve modelling is considered in this paper, where we aim to optimally estimate the underpinning unknown...
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RKHS-based covariate balancing for survival causal effect estimation
Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score...
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Optimal classification methods for diagnosing latent skills and misconceptions for option-scored multiple-choice item quizzes
The paper’s extended Diagnostic Classification Modeling setting assumes (a) nominal item (question) coding, thus including multiple-choice (MC)...
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Goodness of Fit Test and Tests for Contingency Tables
Chapter 6 presents the theory related to the asymptotic null distribution in goodness of fit test procedures and in tests for contingency tables. All...
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Monte Carlo Methods
The term “Monte Carlo methods” or “MC methods” generally refers to all those techniques that make use of artificial (i.e. computer generated) random...
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Ranks, copulas, and permutons
We review a recent development at the interface between discrete mathematics on one hand and probability theory and statistics on the other,...
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M-estimators and trimmed means: from Hilbert-valued to fuzzy set-valued data
Different approaches to robustly measure the location of data associated with a random experiment have been proposed in the literature, with the aim...
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De-identifying Clinical Trial Data
Conducting clinical trials involves collecting detailed health information about participants. Privacy of individual participants is important and...
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Extensions of the Classical Linear Model
This chapter discusses several extensions of the classical linear model. We first describe the general linear model and its applications in Sect. 4.1.
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On a Generalization of Gompertz Distribution and its Applications
Gompertz distribution was proposed by Gompertz in 1825 and he showed that age specific mortality rates increase exponentially with age over much of...
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Introduction to Basket Trials
With the advent of targeted therapies and immunotherapies, precision medicine has entered clinical practice. Basket trials provide an important...
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Modeling Forest Tree Data Using Sequential Spatial Point Processes
The spatial structure of a forest stand is typically modeled by spatial point process models. Motivated by aerial forest inventories and forest...
