### 10th World Congress in Probability and Statistics

## Contributed Session (live Q&A at Track 2, 9:30PM KST)

## Random Structures

### Universal phenomena for random constrained permutations

Jacopo Borga (University of Zurich)

### The scaling limit of the strongly connected components of a uniform directed graph with an i.i.d. degree sequence

Serte Donderwinkel (University of Oxford)

### Spherical principal curves

Jongmin Lee (Seoul National University)

### Q&A for Contributed Session 13

###### Session Chair

Namgyu Kang (Korea Institute for Advanced Study)

## Copula Modeling

### Estimation of multivariate generalized gamma convolutions through Laguerre expansions

Oskar Laverny (Université Lyon 1)

### Copula-based Markov zero-inflated count time series models

Mohammed Alqawba (Qassim University)

### Bi-factor and second-order copula models for item response data

Sayed H. Kadhem (University of East Anglia)

### Q&A for Contributed Session 20

###### Session Chair

Daewoo Pak (Yonsei University)

## Multivariate Data Analysis

### A nonparametric test for paired data

Grzegorz Wyłupek (Institute of Mathematics, University of Wrocław)

### Inference for Generalized Multivariate Analysis of Variance (GMANOVA) models, under multivariate skew t distribution for modelling skewed and heavy-tailed data

Sayantee Jana (Indian Institute of Management Nagpur)

### Multiscale representation of directional scattered data: use of anisotropic radial basis functions

Junhyeon Kwon (Seoul National Universtiy)

### Q&A for Contributed Session 26

###### Session Chair

Yunjin Choi (University of Seoul)

## Statistical Prediction

### Robust geodesic regression

Ha-Young Shin (Seoul National University)

### A multi-sigmoidal logistic model: statistical analysis and first-passage-time application

Paola Paraggio (Università degli Studi di Salerno (UNISA))

However, many real phenomena exhibit different phases, each one following a sigmoidal-type pattern. Stimulated by these more complex dynamics, many researchers investigate generalized versions of classical sigmoidal models characterized by several inflection points.

Along these research lines, a generalization of the classical logistic growth model is considered in the present work, introducing in its expression a polynomial term. The model is described by a stochastic differential equation obtained from the deterministic counterpart by adding a multiplicative noise term. The resulting diffusion process, having a multi-sigmoidal mean, may be useful in the description of particular growth dynamics in which the evolution occurs by stages.

The problem of finding the maximum likelihood estimates of the parameters involved in the definition of the process is also addressed. Precisely, the maximization of the likelihood function will be performed by means of meta-heuristic optimization techniques. Moreover, various strategies for the selection of the optimal degree of the polynomial will be provided.

Further, the first-passage-time (FPT) problem is considered: an approximation of its density function will be obtained numerically, by means of the fptdApprox R-package

Finally, some simulated examples are presented.

### Statistical inference for functional linear problems

Tim Kutta (Ruhr University Bochum)

### Q&A for Contributed Session 31

###### Session Chair

Changwon Lim (Chung-Ang University)