[ABE-L] Seminários no IME/USP - Regression models for limited range data - 16/03/2018, 11h

[Silvia Ferrari]

*Seminários - Projeto Temático: Modelos de Regressão e AplicaçõesRegression
models for limited range dataSeminário 1*

*Título*: Kumaraswamy autoregressive moving average models for double
bounded environmental data

*Palestrante*: Fábio M. Bayer, Depto. de Estatística, Universidade Federal
de Santa Maria


*Seminário 2*

*Título*: On nonlinear beta regression residuals

*Palestrante*: Patrícia L. Espinheira, Depto. de Estatística, Universidade
Federal de Pernambuco


*Quando*: 16 de março de 2018, sexta-feira, às 11h.

*Onde*: Sala 144 – Bloco B, 1o andar - IME-USP


Seguem resumos.

*Seminário 1*. In this paper we introduce the Kumaraswamy autoregressive
moving average models (KARMA), which is a dynamic class of models for time
series taking values in the double bounded interval (a,b) following the
Kumaraswamy distribution. The Kumaraswamy family of distribution is widely
applied in many areas, especially hydrology and related fields. Classical
examples are time series representing rates and proportions observed over
time. In the proposed KARMA model, the median is modeled by a dynamic
structure containing autoregressive and moving average terms, time-varying
regressors, unknown parameters and a link function. We introduce the new
class of models and discuss conditional maximum likelihood estimation,
hypothesis testing inference, diagnostic analysis and forecasting. In
particular, we provide closed-form expressions for the conditional score
vector and conditional Fisher information matrix. An application to
environmental real data is presented and discussed. Joint work with Débora
M. Bayer and Guilherme Pumi.

*Seminário 2*. We proposed a new residual to be used in linear and
nonlinear beta regressions. Unlike the residuals that had already been
proposed, the derivation of the new residual takes into account not only
information relative to the estimation of the mean submodel but also takes
into account information obtained from the precision submodel. This is an
advantage of the residual we introduced. Additionally, the new residual is
computationally less intensive than the weighted residual. Recall that the
computation of the latter involves an n x n matrix, where n is the sample
size. Obviously, that can be a problem when the sample size is very large.
In contrast, our residual does not suffer from that. It can be easily
computed even in large samples. Finally, our residual proved to be able to
identify atypical observations as well as the weighted residual. We also
propose new thresholds for residual plots and a scheme for the choice of
starting values to be used in maximum likelihood point estimation in the
class of nonlinear beta regression models. We report Monte Carlo simulation
results on the behavior of different residuals.We also present and discuss
two empirical applications; one uses the proportion of killed grasshoppers
in an assay on the grasshopper Melanopus sanguinipes with the insecticide
carbofuran and the synergist piperonyl butoxide, which enhances the
toxicity of the insecticide, and the other uses simulated data. The results
favor the new methodology we introduce. Joint work with Evelyne G. Santos,
and Francisco Cribari-Neto.
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