[PDF] Top 20 Statistical inference for the linear model with clustered data
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Statistical inference for the linear model with clustered data
... OLS model predicting challenger spending as a percentage of incumbent spending (Hogan 2008, Table 2, ...spending model, the expectation is that greater divergence will correspond to increased spending by ... See full document
41
The effect on statistical inference of the degree of precision of recorded data
... Sidebottom (1976) gave a method and a FORTRAN program which can be derived from Fisher's method of scoring for M L estimation of location and scale parameters from grouped data. Wolynetz (1979a) described an ... See full document
375
Statistical Nonparametric and Linear Mixed Model Analyses of Oligonucleotide DNA Chips Data
... chip data. This enables rigorous statistical quality control by automatically flagging observations that significantly deviate from the model, as well as quantified statistical ... See full document
101
Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees
... account clustered or nested dataset structures, which are particularly common in psy- chological ...generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection ... See full document
19
Statistical Inference for Correlated Data Based on Censored Observations
... Zeger and Brookmeyer (1986) suggested a full likelihood estimation and an ap- proximate method for a censored autoregressive time series. However, the authors pointed out in their article that the method may not be ... See full document
141
Fixed effects inference for clustered data in Gaussian linear models
... practice, data analysis almost always includes covariates other than the effect of interest, either as a means of controlling for potential confounding or to reduce the residual ...for data with balanced ... See full document
105
Some Inference Problems in Clustered (Longitudinal) Count Data with Over-dispersion
... count data, presented simulation results and showed some ...heterogeneous data, is ...generalized linear mixed effects model and the negative binomial mixed effects model and used ... See full document
118
On Inference of the Linear Regression Model with Groupwise Heteroscedasticity
... heteroscedastic linear regression ...when data are divided into 10, 20 and 30 groups of different sizes and the regression is run on the mean values of the dependent variable and the regressor of these ... See full document
12
Semiparametric inference based on a class of zero-altered distributions
... count data with too few or too many zeros are very important in various scientific fields including but not limited to industrial applications ...a data set that features underdispersion (see Table ...related ... See full document
21
Statistical inference in a random coefficient panel model
... Recently, also due to the increasing availability of large datasets, models with random coe¢ cient have been applied in the context of panel data analysis (see Hsiao and Pesaran, 2004). Although slope het- ... See full document
46
Statistical inference in a directed network model with covariates
... the data can be found in Section ...a model which can characterize the node-specific outgoingness and ...useful model should account for the covariates in order to explain the observed homophily ... See full document
34
Permutation inference for the general linear model
... Permutation inference is grounded on exchangeability under the null hypothesis, that data can be permuted (exchanged) without affecting its joint ...the model, the data cannot be con- sidered ... See full document
18
Duration Modeling in Hindi
... a model for implementing the prosodic variation in text to speech synthesis(TTS) and automatic speech ...the model is purely based on the statistical inference derived from the duration values ... See full document
5
Statistical inference from large scale genomic data
... that model-based clustering methods are preferable to heuristic clustering methods such as K-means and hierarchical clustering for this data ...the model-based clustering algorithms, PMDE and MCLUST ... See full document
206
Statistical inference for movement behaviour using animal tracking data
... This chapter briefly discusses the four elements required for this process: track data; covariate information; a model capable of using a hypothesis of movement behaviour, together with [r] ... See full document
261
Bayesian Inference and Optimal Design for the Sparse Linear Model
... sparse linear model has been proposed as Lasso (Tib- shirani, 1996) and as basis pursuit (Chen et ...a linear programming ...fixed data can be done very efficiently, in fact significantly ... See full document
55
Adaptive Development Data Selection for Log linear Model in Statistical Machine Translation
... translation model or language ...domain data (both monolingual and bilingual) to the existing training corpora has been shown to be very effective in ...But model adaptation is required in more ... See full document
9
Statistical Inference For High-Dimensional Linear Models
... ence of invalid IVs and high dimensional covariates. The key step in the procedure is STEP 2, where we utilize two-stage hard thresholding, to deal with the problem posed by invalid IVs; as such, we call our procedure ... See full document
253
"Smooth" Inference for Clustered Survival Data
... generator. Inference does not depend on the form of the marginal distributions, which are treated as nuisance ...second model in Clayton (1978) and estimated the association parameter in the Plackett ... See full document
164
Some aspects of statistical inference in the linear regression model
... It therefore appears that from a practical point of view, there are few changes of major consequence in the distributions and properties of estimators and the properties of statistical t[r] ... See full document
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