The Multilevel Regression Model

2014 ◽  
pp. 133-152
Author(s):  
Joop Hox ◽  
Leoniek Wijngaards-de Meij
Keyword(s):  
Regression Model ◽  
Multilevel Regression ◽  
Multilevel Regression Model

scholarly journals Development of a Pressure–Precipitation Transmitter

2019 ◽  
Vol 58 (11) ◽  
pp. 2453-2468
Author(s):  
Masaru Inatsu ◽  
Tamaki Suematsu ◽  
Yuta Tamaki ◽  
Naoto Nakano ◽  
Kao Mizushima ◽  
...  
Keyword(s):  
Regression Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Pattern ◽  
Multilevel Regression ◽  
Extreme Statistics ◽  
Spatial Continuity ◽  
Daily Precipitation Data ◽  
Multilevel Regression Model

AbstractA novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

scholarly journals A Multilevel Regression Model for Geographical Studies in Sets of Non-Adjacent Cities

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0133649 ◽  
Author(s):  
Marc Marí-Dell’Olmo ◽  
Miguel Ángel Martínez-Beneito
Keyword(s):  
Regression Model ◽  
Multilevel Regression ◽  
Multilevel Regression Model

Predicting Depression among Patients with Diabetes Using Longitudinal Data

2015 ◽  
Vol 54 (06) ◽  
pp. 553-559 ◽  
Author(s):  
H. Jin ◽  
I. Vidyanti ◽  
P. Di Capua ◽  
B. Wu ◽  
S. Wu
Keyword(s):  
Major Depression ◽  
Regression Model ◽  
Predictive Accuracy ◽  
Patient Records ◽  
Historical Records ◽  
Multilevel Regression ◽  
Population Average ◽  
Subject Specific ◽  
Patients With Diabetes ◽  
Multilevel Regression Model

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.Background: Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression.Objectives: This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes.Methods: Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression.Results: Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions.Conclusions: The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.

scholarly journals Relative Deprivation, Absolute Deprivation, and Homicide: Testing an Interaction Between Income Inequality and Disadvantage

2018 ◽  
Vol 23 (1) ◽  
pp. 3-19
Author(s):  
Bert Burraston ◽  
Stephen J. Watts ◽  
James C. McCutcheon ◽  
Karli Province
Keyword(s):  
Income Inequality ◽  
Regression Model ◽  
Significant Interaction ◽  
Relative Deprivation ◽  
Multilevel Regression ◽  
Multilevel Regression Model

Both relative and absolute deprivation have effects on crime. These two concepts may be complementary, but much scholarship has treated them as separate. The present study assesses whether the effects of relative and absolute deprivation, measured as income inequality and disadvantage, respectively, interact in their effect on known homicide counts in U.S. counties. A multilevel regression model shows that there is a significant interaction between income inequality and disadvantage predicting homicide counts known to police. The plot of this interaction shows that when disadvantage is extremely high, increasing income inequality does not increase known homicides. The less disadvantage there is, the greater the effect of increasing income inequality on homicide counts in U.S. counties. This finding suggests that the effect of relative deprivation on known homicide is contingent on levels of absolute deprivation and vice versa. The implication of this finding is discussed.

scholarly journals Farm Gate Prices for Non-Varietal Wine in Argentina: A Multilevel Comparison of the Prices Paid by Cooperatives and Investor-Oriented Firms

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Agustina Malvido Perez Carletti ◽  
Markus Hanisch ◽  
Jens Rommel ◽  
Murray Fulton
Keyword(s):  
Multilevel Regression ◽  
Data Set ◽  
Unique Data ◽  
Market Shares ◽  
Price Effects ◽  
Theoretical Predictions ◽  
The One ◽  
The Cost ◽  
Price Differences ◽  
Multilevel Regression Model

AbstractIn this paper, we use a unique data set of the prices paid to farmers in Argentina for grapes to examine the prices paid by non-varietal wine processing cooperatives and investor-oriented firms (IOFs). Motivated by contrasting theoretical predictions of cooperative price effects generated by the yardstick of competition and property rights theories, we apply a multilevel regression model to identify price differences at the transaction level and the departmental level. On average, farmers selling to cooperatives receive a 3.4 % lower price than farmers selling to IOFs. However, we find cooperatives pay approximately 2.4 % more in departments where cooperatives have larger market shares. We suggest that the inability of cooperatives to pay a price equal to or greater than the one paid by IOFs can be explained by the market structure for non-varietal wine in Argentina. Specifically, there is evidence that cooperative members differ from other farmers in terms of size, assets and the cost of accessing the market. We conclude that the analysis of cooperative pricing cannot solely focus on the price differential between cooperatives and IOFs, but instead must consider other factors that are important to the members.

scholarly journals Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects

2018 ◽  
Vol 7 (8) ◽  
pp. 325 ◽  
Author(s):  
Luzi Xiao ◽  
Lin Liu ◽  
Guangwen Song ◽  
Stijn Ruiter ◽  
Suhong Zhou
Keyword(s):  
Road Network ◽  
Multilevel Regression ◽  
Individual Level ◽  
Local Residents ◽  
Journey To Crime ◽  
Home Community ◽  
Almost All ◽  
Distinct Features ◽  
Multilevel Regression Model ◽  
Insight Into

Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by.

scholarly journals Seroprevalence of COVID-19 among health workers in the Kathmandu valley, Nepal (SEVID-KaV): a longitudinal cohort study

2021 ◽  
Author(s):  
Kiran Raj Pandey ◽  
Aseem Bhattarai ◽  
Suman Pant ◽  
Rimmy Barakoti ◽  
Janaki Pandey ◽  
...  
Keyword(s):  
Disease Burden ◽  
Health Workers ◽  
Credible Interval ◽  
Longitudinal Survey ◽  
Kathmandu Valley ◽  
Test Accuracy ◽  
Igg Antibodies ◽  
Multilevel Regression ◽  
Occupation Category ◽  
Multilevel Regression Model

Abstract Coronavirus Disease 2019 (COVID-19) burden is often underestimated when relying on case-based incidence reports. Seroprevalence studies accurately estimate infectious disease burden by estimating the population that has developed antibodies following an infection. Sero-Epidemiology of COVID-19 in the Kathmandu valley (SEVID-KaV) is a longitudinal survey of hospital-based health workers in the Kathmandu valley. Between December 3-25, we sampled 800 health workers from 20 hospitals and administered a questionnaire eliciting COVID-19 related history and tested for COVID-19 IgG antibodies. We then used a probabilistic multilevel regression model with post-stratification to correct for test accuracy, the effect of hospital-based clustering, and to establish representativeness. 522 (65.2%) of the participants were female, 372 (46%) were between ages 18-29, and 7 (0.9%) were 60 or above. 287 (36%) of the participants were nurses. About 23% of the participants previously had a PCR positive infection. 321 (40.13%) individuals tested positive for COVID-19 antibodies. Adjusted for test accuracy and weighted by age, gender and occupation category, the seroprevalence was 38.17% (95% Credible Interval (CrI) 29.26%–47.82%). Posterior predictive hospital-wise seroprevalence ranged between 38.1% (95% CrI 30.7.0%– 44.1%) and 40.5% (95% CrI 34.7%–47.0%).

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