scholarly journals ON THE ESTIMATION AND PREDICTION IN MIXED LINEAR MODELS

1998 ◽  
Vol 55 (2) ◽  
pp. 291-295
Author(s):  
L.A. LÓPEZ ◽  
A.F. IEMMA
Keyword(s):  
Linear Model ◽  
Lagrange Multipliers ◽  
Linear Models ◽  
Mixed Effects ◽  
Structural Method ◽  
Mixed Linear Models ◽  
Estimation And Prediction ◽  
Alternative Method ◽  
Linear Predictors

Beginning with the classical Gauss-Markov Linear Model for mixed effects and using the technique of the Lagrange multipliers to obtain an alternative method for the estimation of linear predictors. A structural method is also discussed in order to obtain the variance and covariance matrixes and their inverses.

scholarly journals Detection of Fraudulent Transactions Through a Generalized Mixed Linear Models

2012 ◽  
Vol 8 (16) ◽  
pp. 221-237 ◽  
Author(s):  
Jackelyne Gómez–Restrepo ◽  
Myladis Cogollo–Flórez
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Logistic Model ◽  
Linear Models ◽  
Financial Sector ◽  
Customer Behavior ◽  
Global Model ◽  
Mixed Linear Models ◽  
Intimate Knowledge ◽  
Conventional Methods

The detection of bank frauds is a topic which many financial sector companies have invested time and resources into. However, finding patterns in the methodologies used to commit fraud in banks is a job that primarily involves intimate knowledge of customer behavior, with the idea of isolating those transactions which do not correspond to what the client usually does. Thus, the solutions proposed in literature tend to focus on identifying outliersor groups, but fail to analyse each client or forecast fraud. This paper evaluates the implementation of a generalized linear model to detect fraud. With this model, unlike conventional methods, we consider the heterogeneity of customers. We not only generate a global model, but also a model for each customer which describes the behavior of each one according to their transactional history and previously detected fraudulent transactions. In particular, a mixed logistic model is used to estimate the probability that a transactionis fraudulent, using information that has been taken by the banking systems in different moments of time.

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models

scholarly journals Analysis of a Linear Model for Non-Synchronous Vibrations Near Stall

2021 ◽  
Vol 6 (3) ◽  
pp. 26
Author(s):  
Christoph Brandstetter ◽  
Sina Stapelfeldt
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Structural Vibration ◽  
Experimental Investigations ◽  
Vibration Modes ◽  
Critical Problem ◽  
Safety Critical ◽  
Unstable Vibration ◽  
Aero Engines ◽  
Lock In

Non-synchronous vibrations arising near the stall boundary of compressors are a recurring and potentially safety-critical problem in modern aero-engines. Recent numerical and experimental investigations have shown that these vibrations are caused by the lock-in of circumferentially convected aerodynamic disturbances and structural vibration modes, and that it is possible to predict unstable vibration modes using coupled linear models. This paper aims to further investigate non-synchronous vibrations by casting a reduced model for NSV in the frequency domain and analysing stability for a range of parameters. It is shown how, and why, under certain conditions linear models are able to capture a phenomenon, which has traditionally been associated with aerodynamic non-linearities. The formulation clearly highlights the differences between convective non-synchronous vibrations and flutter and identifies the modifications necessary to make quantitative predictions.

scholarly journals A Method for Assessing Extent and Sources of Heterogeneity of Residual Variances in Mixed Linear Models

1990 ◽  
Vol 73 (6) ◽  
pp. 1612-1624 ◽  
Author(s):  
J.L. Foulley ◽  
D. Gianola ◽  
M. San Cristobal ◽  
S. Im
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Residual Variances
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