Counting Numerical Semigroups by Genus and Even Gaps
Matheus Bernardini, Fernando Torres
We present an approach to count numerical semigroups of a given genus by using even gaps. Our method is motivated by the interplay between double covering of curves and γ-hyperelliptic semigroups , , , , .
On the curve Y^n = X^l (X^m + 1) over finite fields
Saeed Tafazolian, Fernando Torres
Let X be the nonsingular model of a plane curve of type y^n = f (x) over the finite field F of order q^2 , where f(x) is a separable polynomial of degree coprime to n. If the number of F-rational points of X attains the Hasse-Weil bound, then the condition n divides q + 1 is equivalente to the solubility of f(x) in F. In this paper, we investigate this condition for f(x) = x^l (x^m + 1)
On the spectrum for the genera of maximal curves over small fields
Nazar Arakelian, Saeed Tafazolian, Fernando Torres
Motivated by previous computations in Garcia, Stichtenoth and Xing (2000) paper , we discuss the spectrum M(q 2 ) for the genera of maximal curves over finite fields of order q 2 with 7 ≤ q ≤ 16. In particular, by using a result in Kudo and Harashita(2016) paper , the set M(7 2 ) is completely determined.
Finite mixture modeling of censored data using the multivariate Student-t distribution
Víctor H. Lachos, Edgar J. López Moreno, Kun Chen
Finite mixture models have been widely used for the modelling and analysis of data froma heterogeneous population. Moreover, these kind of data can be subjected to some upper and/or lower detection limits because of the restriction of experimental apparatus. Another complication arises when measures of each population depart significantly from normality, for instance, in the presence of heavy tails or atypical observations. For such data structures, we propose a robust model for censored data based on finite mixtures of multivariate Student-t distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically simple yet efficient EM-type algorithm for conducting maximum likelihood estimation of the parameters. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the multivariate truncated Student-t distributions. Further, a general information-based method for approximating the asymptotic covariance matrix of the estimators is also presented. Results obtained from the analysis of both simulated and real data sets are reported to demonstrate the effectiveness of the proposed methodology. The proposed algorithm and methods are implemented in the new R package CensMixReg.
Bayesian estimation, residual analysis and prior sensitivity study for zero-one augmented beta regression model with an application to psychometric data
Danilo Covaes Nogarotto, Caio L. N. Azevedo, Jorge Luiz Bazán
The interest on the analysis of the zero-one augmented beta regres sion (ZOABR) model has been increasing over the last years. In this paper we developed an extensive study on parameter recovery, prior sensitivity and the impact of some factors (scenarios of interest) comparing the Bayesian paradigm with the Maximum Likelihood (ML) approach. Jeffreys-rule, independence Jeffreys and improper priors were compared with usual choices. The results indicate, in a general way, that: the Bayesian approach, under the Jeffreys-rule prior, was as accurate as the ML one. In addition, as expected, the larger the sample size and the lower the variability of the data the more accurate are the parameter estimates. Also, we use the predictive distribution of the response to implement some available residual techniques (previously developed under the frequentist approach). To further illustrate the advantages of our approach, we conduct an analysis of a psychometric real data set including Bayesian residual analysis, where is showed that misleading inference can be obtained when the data is transformed. That is, when the observedzeros and ones are transformed to suitable values and the usual beta regression model is considered, instead using the ZOABR model. Finally, future developments are discussed.
Stochastic Neighborhood Structure in Bayesian Spatial Models
Aline Piroutek, R. Assunção, D. Duarte
Probabilistic Context Neighborhood: A New Tree Topology and Hypothese Tests
Aline Piroutek, D. Duarte, R. Assunção, Aluísio Pinheiro
We introduce the Probabilistic Context Neighborhood model for two dimensional lattices as an extension of the Probabilistic Context Tree model in one dimensional space preserving some of its interesting properties. This model has a variable neighborhood structure with a fixed geometry but varying radius. In this way we are able to compute the cardinality of the set of neighborhoods and use the Pseudo-Likelihood Bayesian Criterion to select an appropriate model given the data. We represent the dependence neighborhood structure as a tree making easier to understand the model complexity. We provide an algorithm to estimate the model that explores the sparse tree structure to improve computational efficiency. We also present an extension of the previous model, the Non-Homogeneous Probabilistic Context Neighborhood model, which allows a spatially changing Probabilistic Context Neighborhood as we move on the lattice.
Dynamic Control of Infeasibility for Nonlinear Programming
Abel S. Siqueira, Francisco A. M. Gomes Neto
An effective way of solving general nonlinear programming problems is the adoption of composite-step strategies that combine a step tangent to the constraints and a normal step, alternating between reducing the objective function value and the norm of the infeasibility. However, this kind of method requires the control of the iterates in order to prevent one step from destroying the progress of the other. In the Dynamic Control of Infeasibility algorithm, proposed by Bielschowsky and Gomes for equality constrained problems, the steps are controlled through the use of the so called Trust Cylinders. We present an extension of this algorithm for solving problems with general constraints. We also show numerical experiments that indicate that the new method has a performance that is comparable to well known nonlinear programing codes.
Modelling the proportion of failed courses and GPA scores for engineering major students
Hildete P. Pinheiro, Rafael P. Maia, Eufrásio A. Lima Neto, Mariana R. Motta
There is special interest on the factors which may contribute for the best academic performance of undergraduate students. Particularly, in Brazil, because of the recent quota system and affirmative action programs implemented by some universities and the Federal Government, this issue has been of great interest. We use here zero-one inflated beta models with heteroscedasticity to model the proportion of failed courses taken by Engineering major students at the State University of Campinas, Brazil. We also model the grade point average score for those students with a heteroscedastic skew t distribution. The database consists of records of 3,549 students with Engineering major who entered in the University from 2000 to 2005. The entrance exam score in each subject, some academic variables and their socioeconomic status are considered as covariates in the models. A residual analysis based on randomized quantile residuals is performed as well. Finally, we believe that the results found in this study can be useful to improve the university polices for new students since it was possible to identify student profiles with respect to their academic performance.
Censored Regression Models with Autoregressive Errors: A Likelihood-Based Perspective
Fernanda L. Schumacher, Víctor H. Lachos, Dipak K. Dey
In many studies that involve time series variables, limited or censored data are naturallycollected. This occurs, in several practical situations, for reasons such as limitations of mea-suring equipment or from experimental design. Hence, the exact true value is recorded only ifit falls within an interval range, so the responses can be either left, interval or right censored.Practitioners commonly disregard censored data cases or replace these observations with somefunction of the limit of detection, which often results in biased estimates. In this paper, wepropose an analytically tractable and efficient stochastic approximation of the EM (SAEM)algorithm to obtain the maximum likelihood estimates of the parameter of censored regressionmodels with autoregressive errors of order p. This approach permits easy and fast estimationof the parameters of autoregressive models when censoring is present and as a byproduct, en-ables predictions of unobservable values of the response variable. The observed informationmatrix is derived analytically to account for standard errors. We use simulations to investigatethe asymptotic properties of the SAEM estimates and prediction accuracy. In this simulationstudy comparisons are also made between inferences based on the censored data and thosebased on complete data obtained by crude/ad-hoc imputation methods. Finally, the method isillustrated using a meteorological time series dataset on cloud ceiling height, where the mea-surements are subject to the detection limit of the recording device. The proposed algorithmand methods are implemented in the new R package ARCensReg.