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sensitivity uncertainty in microphone pressure reciprocity calibration. Metrologia, 50 (2), 170-179. [10] Pinheiro, J.C., Bates, D.M. (2000). Mixed-effects Models in S and S-PLUS. Springer. [11] Searle, S.R., Casella, G., McCulloch, C.E. (1992). Variance Components. John Wiley & Sons. [12] Burdick, R.K., Graybill, F.A. (1992). Confidence Intervals on Variance Components. Marcel Dekker. [13] West, B.T., Welch, K.B., Gałecki, A.T. (2007). Linear Mixed Models: A Practical Guide Using Statistical Software. Chapman and Hall/CRC. [14] Witkovský, V. (2012). Estimation, testing

References Bates D., Mächler M., Bolker B.,Walker S., 2015, Fitting Linear Mixed-Effects Models Using Lme4, Journal Of Statistical Software, Volume 67, Issue 1, Doi: 10.18637/Jss.V067.I01. Bentsen Hb., Klemetsdal G., 1991, The Use Of Fixed Effect Models And Mixed Models To Estimate Single Gene Associated Effects On Polygenic Traits, Agricultural University Of Norway, Norway. Biecek P., 2013, Analiza Danych Z Programem R. Modele Liniowe Z Efektami Stałymi, Losowymi I Mieszanymi (Data Analysis With The R Program. Linear Models With Fixed, Random And Mixed Effects

. Santamaría. 2007. “Estimation of the Mean Squared Error of Predictors of Small Area Linear Parameters under a Logistic Mixed Model.” Computational Statistics and Data Analysis 51: 2720–2733. Doi: http://dx.doi.org/10.1016/j.csda.2006.01.012 . González-Manteiga, W., M.J. Lombardía, I. Molina, D. Morales, and L. Santamaría. 2008a. “Bootstrap Mean Squared Error of Small-Area EBLUP.” Journal of Statistical Computation and Simulation 78: 443–462. Doi: http://dx.doi.org/10.1080/00949650601141811 . González-Manteiga, W., M.J. Lombardía, I. Molina, D. Morales, and L

-independence – Generalized Additive Mixed Models (GAMMs) – to a problem of temporal non-independence: that sequential instances of a varying linguistic item in conversational speech are not independent. Instead, temporally proximal instances of a sociolinguistic variable are more likely to surface in the same form than are instances that occur further apart ( Sankoff and Laberge 1978 ). We distinguish two general classes of mechanisms that can give rise to such temporal clustering. First are what we term sequential dependence mechanisms: those in which the outcome of a sociolinguistic

1 Introduction The relationship between a quantitative risk factor and an outcome may take many different functional forms. To avoid the bias induced by misspecifying the functional form and the loss of efficiency in testing produced by categorizing continuous variables, nonparametric (flexible) regression models are often used to model the effects of continuous covariates [ 1 , 2 ]. Generalized linear mixed models (GLMMs) [ 3 ], primarily used for analyzing overdispersed and correlated data (e.g. longitudinal data), can also be used for smoothing [ 4 , 5 ]. To

Volume 3, Issue 1 2008 Article 19 Chemical Product and Process Modeling Transport in Highly Heterogeneous Porous Media: From Direct Simulation to Macro- Scale Two-Equation Models or Mixed Models Debenest Gérald, Institut de Mécanique des Fluides de Toulouse Michel Quintard, Institut de Mécanique des Fluides de Toulouse Recommended Citation: Gérald, Debenest and Quintard, Michel (2008) "Transport in Highly Heterogeneous Porous Media: From Direct Simulation to Macro-Scale Two-Equation Models or Mixed Models," Chemical Product and Process Modeling: Vol. 3: Iss. 1

7 Mixed Models The pure models of chapter 2 for adverse selection, chapter 4 for moral hazard and chapter 6 for nonverifiability were highly stylized contracting settings. Each of those models aimed at capturing a single dimension of the incentive problems that may be faced by a principal at the time of designing the contract for his agent. In those chapters, the analysis of each of these respective paradigms has already provided a number of important insights that concern, on the one hand, the conflict (if any) between allocative efficiency and the distribution

Volume 6, Issue 1 2007 Article 19 Statistical Applications in Genetics and Molecular Biology Using Linear Mixed Models for Normalization of cDNA Microarrays Philippe Haldermans, Hasselt University Ziv Shkedy, Hasselt University Suzy Van Sanden, Hasselt University Tomasz Burzykowski, Hasselt University Marc Aerts, Hasselt University Recommended Citation: Haldermans, Philippe; Shkedy, Ziv; Van Sanden, Suzy; Burzykowski, Tomasz; and Aerts, Marc (2007) "Using Linear Mixed Models for Normalization of cDNA Microarrays," Statistical Applications in Genetics and

]Rr 1 Introduction Many common statistical models can be expressed as linear models that incorporate both fixed effects, which are parameters associated with an entire population or with certain repeatable levels of experimental factors, and random effects, which are associated with individual experimental units drawn at random from a population. A model with both fixed effects and random effects is called a linear mixed model (LMM) [ 45 ]. These models are often used to analyze data in a broad spectrum of areas including clustered data such as longitudinal data

1 Introduction Mixing in flow reactors can be categorized according to levels of “macro-mixing” and “micro-mixing”, as shown in Figure 1 . Danckwerts (1958) popularized the concepts of a residence time distribution (RTD) and the degree of micro-mixing. Zwietering (1959) extended these concepts with the Maximum Mixedness Model (MMM). The perfectly mixed reactor is fully mixed on all levels, and can be referred to as a perfectly stirred reactor (PSR). Reactors along the vertical stirred tanks boundary line are perfectly macro-mixed, but have various degrees of