[PDF] Top 20 Estimation of a multivariate mean under model selection uncertainty
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Estimation of a multivariate mean under model selection uncertainty
... models under consideration. This leads to the class of model averaging ...after model selection. We refer to these estimators as post-model selection estimators (PMSE, Leeb and ... See full document
15
Model selection uncertainty and detection of threshold effects
... VAR model with a structural break in its constant term and established that in general the lag length estimated from a linear VAR will overfit the true lag ...preliminary estimation stage will have on the ... See full document
26
Robust estimation of multivariate location and scatter with application to financial portfolio selection
... linear model, y = X T/3, where y is the ( 6 x 1 ) response vector and X the (6 x 2) matrix of carriers, including the dependent variable and a constant term, is ... See full document
151
Static Mean-Variance portfolio optimization under general sources of uncertainty
... portfolio selection models, it is common that the quantity of each asset is not the same at the beginning and at the end of the investment ...our model, an exogenous factor can also change the quantities, ... See full document
16
Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization
... input uncertainty (observa- tional, conceptual and technical ...measurement uncertainty propagation in implicit 3-D geological ...for uncertainty estimation (MCUE), a stochastic method which ... See full document
18
Parameter estimation and uncertainty quantification for an epidemic model
... Our final sampling method investigated the impact of removing a single data point as a means of identifying the data points which provide the most information for the estimation of the parameters. A baseline data ... See full document
34
Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach
... One of the objectives of modelling time series data is to forecast future values of the variables of interest. The most common procedure for constructing forecasts in time series models is to use conditional expectations ... See full document
22
On model selection in data envelopment analysis: a multivariate statistical approach
... the model, but this is not devoid of ...the model, the more data is needed to obtain reliable results; see Pedraja et al ...we mean the lowest value of an input and the highest value of an ...the ... See full document
31
Robust portfolio selection problem under temperature uncertainty
... the uncertainty sets. Notice that in case of the ellipsoidal uncertainty sets with zero means and the unity covariance ma- trices of the uncertain error coefficients, the robust counterparts of the ... See full document
43
A Procurement Auction Model Under Supplier Uncertainty
... the model of Seshadri et ...supplier selection. More recently, Seshadri (1995) analyzed the supplier selection and control problem in an integrated fash- ...two-period model, they explored the ... See full document
30
Quantile forecasts of inflation under model uncertainty
... Bayesian mean regression as well as the Bayesian quantile regression models with various ...AR(2) model estimated with noninformative prior (“AR(2)” case ) ...the mean regression and quantile ... See full document
9
Consistent estimation in the bilinear multivariate errors in variables model
... the mean square error of estimation eðmÞ for LS (dotted line), ALS (solid line), small sample modified ALS (dashed-dotted line) and partial LS (dashed lines) esti- ... See full document
33
Oil Price Uncertainty in the Iranian Economy
... a multivariate GARCH-in-mean VAR with two lags, using annually observations on the log change in the real price of oil and the log change in real GDP over 1965 to 2013 for the economy of ...GARCH-in- ... See full document
16
Estimation of Multivariate Sample Selection Models via a Parameter Expanded Monte Carlo EM Algorithm
... This paper is organized as follows. The multivariate sample selection model (MSSM) is formulated in Section 2. Section 3 begins with a brief overview of the EM algorithm for the MSSM and continues ... See full document
7
Inflation and Inflation Uncertainty in Iran: An Application of GARCH-in-Mean Model with FIML Method of Estimation
... On the empirical side of the inflation uncertainty literature, the results are mixed (see e.g. Golob, 1995; Baillie et al., 1996; Crawford and Kasmovich, 1996; Joyce, 1997; Grier and Perry, 1990, 1998, 2000; Davis ... See full document
16
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
... There are many missing values in this data set, corresponding to missed votes. Since our analysis depends on data values taken solely from {− 1,1 } , it was necessary to impute values to these. For this experiment, we ... See full document
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Model and distribution uncertainty in multivariate GARCH estimation: a Monte Carlo analysis
... and multivariate t BEKK models largely overestimate the Value at ...a multivariate t distribution for the innovations, we registered a rejection rate in the DQ test of 74% and 52% for the scalar and ... See full document
26
The Determinants of India’s Imports: A Gravity Model Approach
... all the observations would be treated as equal and a pooled model would be estimated using OLS. The requirement for this strategy is a constant coefficient across time. Another approach could be to allow for ... See full document
16
Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... In this paper we explicitly derive the score and Hessian matrix for the multivariate normal mixture model, and use the results to estimate the infor- mation matrix. This provides a twofold extension of ... See full document
26
Project selection problem under uncertainty: An application of utility theory and chance constrained programming to a real case Pages 373-385 Download PDF
... Assuming that individuals have consistent preferences over an appropriate set of lotteries, Von Neumann & Morgenstern (1944), Friedman & Savage (1948), Savage (1954), and Anscombe and Aumann (1963) showed that ... See full document
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