957 research outputs found
State space mixed models for longitudinal obsservations with binary and binomial responses
We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seaonal or other time-varying covariates while preserving the power of state space models in modeling serial dependence in the data. We develop a Markov Chain Monte Carlo algorithm to carry out statistical inferene for models with binary and binomial responses, in which we invoke de Jong and Shephard's (1995) simulaton smoother to establish an efficent sampling procedure for the state variables. To quantify and control the sensitivity of posteriors on the priors of variance parameters, we add a signal-to-noise ratio type parmeter in the specification of these priors. Finally, we ilustrate the applicability of the proposed state space mixed models for longitudinal binomial response data in both simulation studies and data examples
Time-Deformation Modeling Of Stock Returns Directed By Duration Processes
This paper presents a new class of time-deformation (or stochastic volatility) models for stock returns sampled in transaction time and directed by a generalized duration process. Stochastic volatility in this model is driven by an observed duration process and a latent autoregressive process. Parameter estimation in the model is carried out by using the method of simulated moments (MSM) due to its analytical feasibility and numerical stability for the proposed model. Simulations are conducted to validate the choices of the moments used in the formulation of the MSM. Both the simulation and empirical results obtained in this paper indicate that this approach works well for the proposed model. The main empirical findings for the IBM transaction return data can be summarized as follows: (i) the return distribution conditional on the duration process is not Gaussian, even though the duration process itself can marginally function as a directing process; (ii) the return process is highly leveraged; (iii) a longer trade duration tends to be associated with a higher return volatility; and (iv) the proposed model is capable of reproducing return whose marginal density function is close to that of the empirical return.Duration process; Ergodicity; Method of simulated moments; Return process; Stationarity.
Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data
This paper focuses on efficient estimation, optimal rates of convergence and effective algorithms in the partly linear additive hazards regression model with current status data. We use polynomial splines to estimate both cumulative baseline hazard function with monotonicity constraint and nonparametric regression functions with no such constraint. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound. In addition, we show that rates of convergence for the estimators of nonparametric functions are optimal. We implement the proposed estimation through a backfitting algorithm on generalized linear models. We conduct simulation studies to examine the finiteāsample performance of the proposed estimation method and present an analysis of renal function recovery data for illustration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/1/sjos12108.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/2/sjos12108-sup-0001-supinfo.pd
fastMI: a fast and consistent copula-based estimator of mutual information
As a fundamental concept in information theory, mutual information (MI) has
been commonly applied to quantify association between random variables. Most
existing estimators of MI have unstable statistical performance since they
involve parameter tuning. We develop a consistent and powerful estimator,
called fastMI, that does not incur any parameter tuning. Based on a copula
formulation, fastMI estimates MI by leveraging Fast Fourier transform-based
estimation of the underlying density. Extensive simulation studies reveal that
fastMI outperforms state-of-the-art estimators with improved estimation
accuracy and reduced run time for large data sets. fastMI provides a powerful
test for independence that exhibits satisfactory type I error control.
Anticipating that it will be a powerful tool in estimating mutual information
in a broad range of data, we develop an R package fastMI for broader
dissemination
Adaptive Bootstrap Tests for Composite Null Hypotheses in the Mediation Pathway Analysis
Mediation analysis aims to assess if, and how, a certain exposure influences
an outcome of interest through intermediate variables. This problem has
recently gained a surge of attention due to the tremendous need for such
analyses in scientific fields. Testing for the mediation effect is greatly
challenged by the fact that the underlying null hypothesis (i.e. the absence of
mediation effects) is composite. Most existing mediation tests are overly
conservative and thus underpowered. To overcome this significant methodological
hurdle, we develop an adaptive bootstrap testing framework that can accommodate
different types of composite null hypotheses in the mediation pathway analysis.
Applied to the product of coefficients (PoC) test and the joint significance
(JS) test, our adaptive testing procedures provide type I error control under
the composite null, resulting in much improved statistical power compared to
existing tests. Both theoretical properties and numerical examples of the
proposed methodology are discussed
Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135531/1/biom12496.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135531/2/biom12496_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135531/3/biom12496-sup-0001-SuppData.pd
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