scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

scholarly journals P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A45-A45
Author(s):  
J Leota ◽  
D Hoffman ◽  
L Mascaro ◽  
M Czeisler ◽  
K Nash ◽  
...  
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Natural Experiment ◽  
National Basketball Association ◽  
Home Team ◽  
The Road ◽  
On The Road ◽  
The Impact ◽  
The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

scholarly journals Using mixed models to combine smooth size-selection and catch-comparison curves over hauls

2003 ◽  
Vol 60 (4) ◽  
pp. 448-459 ◽  
Author(s):  
R J Fryer ◽  
A F Zuur ◽  
N Graham
Keyword(s):  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Categorical Variable ◽  
Size Selection ◽  
Research Survey ◽  
Different Levels ◽  
Selection Of ◽  
Comparison Data

Parametric size-selection curves are often combined over hauls to estimate a mean selection curve using a mixed model in which between-haul variation in selection is treated as a random effect. This paper shows how the mixed model can be extended to estimate a mean selection curve when smooth nonparametric size-selection curves are used. The method also estimates the between-haul variation in selection at each length and can model fixed effects in the form of the different levels of a categorical variable. Data obtained to estimate the size-selection of dab by a Nordmøre grid are used for illustration. The method can also be used to provide a length-based analysis of catch-comparison data, either to compare a test net with a standard net or to calibrate two research survey vessels. Haddock data from an intercalibration exercise are used for illustration.

scholarly journals Impact of Full IFRS, Accounting Standards for SMES and Company Demographics on Firms’ Return on Asset and Return on Equity using Panel Data Regression

2019 ◽  
Vol 8 (6S) ◽  
pp. 589-601 ◽  
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Ordinary Least Squares ◽  
Lagrangian Multiplier ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Real Property ◽  
The Impact

Due to globalization, markets are becoming more interconnected as the companies are engaged in doing cross-border offerings. Currently, competitions are intensified because Domestic organizations discover themselves competing with each nearby opposite numbers and worldwide companies. But one component that hinders SMEs is the need for reliable and similar monetary data. According to Abarca (2014), adoption of a high-quality and consistent set of accounting requirements is critical so as for the businesses to remain competitive in ASEAN member states. This paper ambitions to answer the query, what modified into the extent of the impact of compliance with full IFRS and IFRS for SMEs on profitability of agencies belong to real property enterprise? This paper moreover sought to decide whether there may be a sizeable distinction among the groups’ compliance with the overall PFRS and the PFRS for SMEs and to determine whether or now not there is a massive distinction among the companies’ financial normal overall performance earlier than and after the adoption of the PFRS for SMEs.Paired T-test have become employed in case you need to determine whether there is a big distinction between the agencies’ compliance with the entire PFRS and the PFRS for SMEs and to decide whether or not there may be a big difference some of the groups’ monetary performance earlier than and after the adoption of the PFRS for SMEs. Using STATA, the great appropriate version for every economic ratio on the subject of degree of compliance emerge as determined on. First, take a look at parm command became used to find out which most of the Least Squares Dummy Variable Regression Modes (LSDV1, LSDV2, LSDV3) underneath the Fixed Effects Model is the ideal version. Afterwards, Hausman Fixed Random Test changed into used to pick out out which is more suitable amongst Fixed Effects Model and Random Effects Model. If Fixed Effects Model modified into the more appropriate one, the Wald’s test turn out to be used to determine the best version among Fixed Effects Model and Ordinary Least Squares Model. On the alternative hand, if Random Effects Model became the more suitable one, the Breusch and Pagan Lagrangian Multiplier Test for Random Effect have become used to decide the satisfactory version amongst Random Effects Model and Ordinary Least Squares. Moreover, if Ordinary Least Squares became the splendid model, it is going to be in addition tested to check for heteroscedasticity and multicollinearity. White’s test became used to check for heterescedasticity and Variance Inflation Factor have become used to test if multicollinearity is gift. The results display that the adoption of PFRS for SMEs stepped forward the compliance of Philippine real property SMEs. However, no vast alternate became said inside the financial average performance of those companies (as measured with the resource of cross back on assets and go back on equity). This was further supported by the results of the panel regression. This means that despite having a relatively

scholarly journals The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

2021 ◽  
Vol 12 ◽  
Author(s):  
Soyoung Kim ◽  
Yoonhwa Jeong ◽  
Sehee Hong
Keyword(s):  
Random Effects ◽  
Hierarchical Linear Modeling ◽  
Fixed Effects ◽  
Random Effect ◽  
Information Criteria ◽  
Linear Modeling ◽  
Random Effect Modeling ◽  
Error Bias ◽  
The Impact ◽  
Level 2

The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model.

scholarly journals Analysis of lactation feed intakes for sows with extended lactation lengths

2017 ◽  
Vol 1 (1) ◽  
pp. 1-25
Author(s):  
F. A. Cabezón ◽  
A. P. Schinckel ◽  
Y. L. León ◽  
B. A. Craig
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Polynomial Function ◽  
Random Effect ◽  
Information Criteria ◽  
Polynomial Functions ◽  
Feeding System ◽  
Lactation Length ◽  
Akaike’S Information Criteria

Abstract The objectives of this research were to quantify and model daily feed intakes to 28 d of lactation in modern sows. A total of 4,512 daily feed intake (DFI) records were collected for 156 Hypor sows from February 2015 to March 2016. The mean lactation length was 27.9 ± 2.0 d. The data included 9 parity 1, 33 parity 2 and 114 parity 3+ sows. Data were collected using a computerized feeding system (Gestal Solo, JYGA Technologies, Quebec, Canada). The feeding system was used to set an upper limit to DFI for the first 7 d of lactation. Overall, the least-squares means of a model including the random effect of sow indicated that DFI's continued to slowly increase to 28 d of lactation. The DFI data were fitted to Generalized Michaelis-Menten (GMM) and polynomial functions of day of lactation (t). The GMM function [DFIi,t (kg/d) = DFI0 + (DFIA − DFI0)(t/K)C/[1 + (t/K)C]] was fitted with 2 random effects for DFI (dfiAi) and intercept (dfi0i) using the NLMIXED procedure in SAS®. The polynomial function DFIi,t (kg/d) = [B0 + B1 t + B2 t2 + B3 t3 + B4 t4] was fitted with three random effects for B0, B1, and B2 using the MIXED procedure in SAS®. Fixed effects models of the two functions had similar Akaike's Information Criteria (AIC) values and mean predicted DFI's. The polynomial function with 3 random effects provided a better fit to the data based on R2 30 (0.81 versus 0.79), AIC (14,709 versus 15,158) and RSD (1.204 versus 1.321) values than the GMM function with two random effects. The random effect for B2 in the polynomial function allowed for the fitting of the function to lactation records that had decreased DFI after 15 d of lactation. The random effects for the polynomial function were used to sort the lactation records into three groups based on the derivative of the function at 21 d of lactation. Lactation records of the three groups had similar DFI the first two weeks of lactation (P > 0.40). The three groups of sows had substantially different DFI's after 18 d of lactation (P < 0.028). The differences in both actual and predicted DFI's between the three groups increased with each day of lactation to day 28 (P < 0.001). Mixed model polynomial functions can be used to identify sows with different patterns of DFI after 15 d of lactation.

scholarly journals A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA

2020 ◽  
Vol 36 (4) ◽  
pp. 707-750 ◽  
Author(s):  
Jinfeng Xu ◽  
Mu Yue ◽  
Wenyang Zhang
Keyword(s):  
Multilevel Modeling ◽  
Random Effects ◽  
Survival Data ◽  
Birth Interval ◽  
Fixed Effects ◽  
Estimation Procedure ◽  
Unknown Parameters ◽  
Modeling Approach ◽  
Clustered Survival Data ◽  
The Impact

In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

scholarly journals 46 Novel creep area heat source impact on pre-wean mortality occurrence

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 34-35
Author(s):  
Benjamin Smith ◽  
Brett Ramirez ◽  
Laura L Greiner
Keyword(s):  
Heat Source ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Experimental Unit ◽  
Mortality Data ◽  
Accounting System ◽  
Swine Industry ◽  
Data Set ◽  
The Impact

Abstract Pre-wean mortality (PWM) is a multifaceted problem facing the US swine industry. The objective of this study was to compare the occurrence of mortality and age of piglets between a novel heat source (HS; semi-enclosed heated microclimate; SEHM) and a conventional heat lamp setup. This study was conducted in two rooms at a 1,000 head commercial sow farm. Treatments were blocked by sow parity group (SPG; young, prime, and geriatric) over six farrowing cycles. Litters were cross fostered within HS treatment at 1 day of age. PWM, was the experimental unit, data were collected from the farm’s farrowing records and production accounting system. Age of the mortalities was categorized into four phases of lactation (PL) over the 20 d lactation (d0 to d3, d4 to d7, d8 to d11, and d12 to d20). Overall, there were 220 recorded instances of mortality. Data were analyzed in a CRBD using a PROC MIXED model in SAS (v9.4, SAS Inst., Cary, NC) and reported as LSMEANS with standard errors. The model included farrowing cycle as a random effect and the following fixed effects: HS, SPG, PL, HS*SPG, and HS*PL. Overall average PWM was 8.60%±0.80 for SEHM and 10.0%±0.70 for the heat lamps. There was no effect (P >0.05) of HS or HS*PL on PWM occurrence. There were significant effects of SPG (P=0.026), PL (P=0.05), and HS*SPG (P=0.036) on PWM occurrence. These data suggest that the HS type did not have an impact on the mortality rate or the timing of the mortality, indicating the mortalities that occurred had no relationship to HS. A limitation of this data set is the low sample size, thereby limiting the detection ability for this interaction. Further research into the impact of the HS is needed to expand the data set and to explore other popular HS effects on PWM.

scholarly journals Random effects regression trees for the analysis of INVALSI data

2021 ◽  
pp. 29-34
Author(s):  
Giulia Vannucci ◽  
Anna Gottard ◽  
Leonardo Grilli ◽  
Carla Rampichini
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Regression Trees ◽  
Linear Component ◽  
Non Linear ◽  
Effect Regression ◽  
Tree Component

Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.

scholarly journals A Flexible Mixed Model for Clustered Count Data

Stats ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 52-69
Author(s):  
Darcy Steeg Morris ◽  
Kimberly F. Sellers
Keyword(s):  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Mixed Models ◽  
Poisson Regression ◽  
Mixed Model ◽  
Random Effect ◽  
Model Fit ◽  
Cross Sectional ◽  
Special Cases

Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson random intercept model, including choice of the random effect distribution, via simulated and real data examples. We find that COM-Poisson mixed models provide comparable model fit to well-known mixed models for associated special cases of clustered discrete data, and result in improved model fit for data with intermediate levels of over- or underdispersion in the count mechanism. Accordingly, the proposed models are useful for capturing dispersion not consistent with commonly used statistical models, and also serve as a practical diagnostic tool.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

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