7,249 research outputs found
A general class of zero-or-one inflated beta regression models
This paper proposes a general class of regression models for continuous
proportions when the data contain zeros or ones. The proposed class of models
assumes that the response variable has a mixed continuous-discrete distribution
with probability mass at zero or one. The beta distribution is used to describe
the continuous component of the model, since its density has a wide range of
different shapes depending on the values of the two parameters that index the
distribution. We use a suitable parameterization of the beta law in terms of
its mean and a precision parameter. The parameters of the mixture distribution
are modeled as functions of regression parameters. We provide inference,
diagnostic, and model selection tools for this class of models. A practical
application that employs real data is presented.Comment: 21 pages, 3 figures, 5 tables. Computational Statistics and Data
Analysis, 17 October 2011, ISSN 0167-9473
(http://www.sciencedirect.com/science/article/pii/S0167947311003628
Kumaraswamy autoregressive moving average models for double bounded environmental data
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 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.Comment: 25 pages, 4 tables, 4 figure
An Extension of Generalized Linear Models to Finite Mixture Outcome Distributions
Finite mixture distributions arise in sampling a heterogeneous population.
Data drawn from such a population will exhibit extra variability relative to
any single subpopulation. Statistical models based on finite mixtures can
assist in the analysis of categorical and count outcomes when standard
generalized linear models (GLMs) cannot adequately account for variability
observed in the data. We propose an extension of GLM where the response is
assumed to follow a finite mixture distribution, while the regression of
interest is linked to the mixture's mean. This approach may be preferred over a
finite mixture of regressions when the population mean is the quantity of
interest; here, only a single regression function must be specified and
interpreted in the analysis. A technical challenge is that the mean of a finite
mixture is a composite parameter which does not appear explicitly in the
density. The proposed model is completely likelihood-based and maintains the
link to the regression through a certain random effects structure. We consider
typical GLM cases where means are either real-valued, constrained to be
positive, or constrained to be on the unit interval. The resulting model is
applied to two example datasets through a Bayesian analysis: one with
success/failure outcomes and one with count outcomes. Supporting the extra
variation is seen to improve residual plots and to appropriately widen
prediction intervals
Asset selection using Factor Model and Data Envelope Analysis - A Quantile Regression approach
With the growing number of stocks and other financial instruments in the investment market, there is always a need for profitable methods of asset selection. The Fama-French three factor model, makes the problem of asset selection easy, by narrowing down the number of parameters, but the usual technique of Ordinary Least Square (OLS), used for estimation of the coefficients of the three factors suffers from the problem of modelling using the conditional mean of the distribution, as is the case with OLS. In this paper, we use the technique of Data Envelopment Analysis (DEA) applied to the Fama-French Three Factor Model, to choose stocks from Dow Jones Industrial Index. We use a more robust technique called as Quantile Regression to estimate the coefficients for the factor model and show that the assets selected using this regression method form a higher return equally weighted portfolio.Asset Selection, Factor Model, DEA, Quantile Regression
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