Comparison of Model Selection Method Using Data from Classical Designed Experiment

2013 ◽  
Vol 677 ◽  
pp. 357-362
Author(s):  
Natthasurang Yasungnoen ◽  
Patchanok Srisuradetchai
Keyword(s):  
Model Selection ◽  
Quantitative Research ◽  
Selection Procedure ◽  
Information Criterion ◽  
P Value ◽  
Selection Procedures ◽  
Final Model ◽  
Designed Experiment ◽  
Using Data ◽  
Selection Of

Model selection procedures play important role in many researches especially quantitative research. . In several area of sciences, the analysis and model selection of experiments are often used and often contains two fundamental goals associated with the experimental response of interest which are to determine the best model. The way to address these goals is to implement a model selection procedure. Then, the objectives of this research are to determine whether or not the final models selected are in agreement or differ substantially across the three approaches to model selection: using Akaike’s Information Criterion, using a p-value criterion, and using a stepwise procedure.. Generally, results from these three models are usually compare to each other. All selected models are based on the heredity principle to design the possible model for each design. The actual data from literature, consisting of the 2x3 and 32 and 3x4 factorial designs are used to determine the final model. The results show that the P-Value WH and Stepwise methods give the highest percentage of matched model.

scholarly journals The Optimal Selection for Restricted Linear Models with Average Estimator

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Qichang Xie ◽  
Meng Du
Keyword(s):  
Model Selection ◽  
Linear Models ◽  
Weighted Average ◽  
Selection Procedure ◽  
Information Criterion ◽  
Optimal Weights ◽  
Model Average ◽  
Squared Error ◽  
Generalized Information Criterion ◽  
Risk Investment

The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing ak-class generalized information criterion (k-GIC), which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.

scholarly journals 'n psigometriese oudit op ‘n bestaande keuringsprosedure

2002 ◽  
Vol 28 (3) ◽  
Author(s):  
E. Cross ◽  
W. Marais ◽  
H. Steel ◽  
C. C. Theron
Keyword(s):  
Selection Procedure ◽  
Developmental History ◽  
Selection Procedures ◽  
Ideal Approach ◽  
History Of ◽  
Selection Of

The validity and credibility of assertions on the efficiency and equity of selection procedures is dependent on the methodology with which the procedure was developed and justified. An ideal approach to the development and justification of a selection procedure was derived from standard guidelines and operationalized in the form of a comprehensive checklist. A psychometric audit on the developmental history of the selection procedure for the selection of commission advisors was undertaken. Various shortcomings were identified and rectified or recommendations were made on rectifying them. The audit found that the selection procedure had zero validity, negative utility and discriminated unfairly. Opsomming Die geldigheid en geloofwaardigheid van uitsprake oor die effektiwiteit en billikheid van ‘n keuringsprosedure is ‘n funksie van die metodologie waarmee die prosedure ontwikkel en regverdig is. ‘n Ideale benadering tot die ontwikkeling en regverdiging van ‘n keuringsprosedure is uit standaard riglyne afgelei en geoperasionaliseer in die vorm van ‘n omvattende kontrolelys. ‘n Psigometriese oudit is onderneem op die ontwikkelingsgeskiedenis van ‘n keuringsprosedure vir die keuring van kommissie-adviseurs. Verskeie tekortkominge is geïdentifiseer en reggestel of aanbevelings ten opsigte van regstelling is gemaak. Die oudit het bevind dat die keuringsprosedure oor zero geldigheid beskik, negatiewe nutwaarde toon en onbillik diskrimineer.

scholarly journals The effect of continued selection of phenotypic intermediates on gene frequency

1964 ◽  
Vol 5 (3) ◽  
pp. 341-353 ◽  
Author(s):  
R. N. Curnow
Keyword(s):  
Gene Frequency ◽  
Selection Procedure ◽  
High Variability ◽  
Considerable Importance ◽  
Selection Procedures ◽  
Population Mean ◽  
Section 8 ◽  
Selection Of

The selection of animals or plants for high values of a certain character may favour not only genotypes associated with these high values but also genotypes associated with high variability. Any differences between genotypes in variability may therefore be of considerable importance in plant and livestock improvement programmes as well as in evolution. The effects of various selection procedures on variability have been studied in three recent experiments [Falconer & Robertson (1956) Falconer (1957) and Prout (1962)]. In these experiments one line was continued by selecting, in each generation, parents with values of a particular character near the population mean. Manning (1955, 1956) has described the effects of this kind of selection applied to cotton. Robertson (1956) derived and discussed the theory of such selection procedures when certain simplifying approximations can be made We shall obtain some more general results and show that Robertson was incorrect in saying that the selection procedure would lead to gene fixation even if the heterozygotes are less variable than the homozygotes. The importance of the results is discussed in section 8.

scholarly journals PROCEDURA KONKURSOWEGO DOBORU KADR KIEROWNICZYCh. PROBLEMATYKA PRAWNA

2016 ◽  
Vol 12 (3) ◽  
pp. 79
Author(s):  
Tomasz Duraj
Keyword(s):  
Significant Influence ◽  
Selection Procedure ◽  
Legal Issues ◽  
Legal Regulations ◽  
Legal Analysis ◽  
Selection Procedures ◽  
Competitive Selection ◽  
The Subject ◽  
Management Staff ◽  
Selection Of

THE COMPETITIVE SELECTION PROCEDURE FOR MANAGEMENT STAFF: LEGAL ISSUES Summary This analysis relates to the legal issues in the competitive selection of management staff. Under the current provisions in Poland many legal acts pertain to this issue, giving an inhomogeneous set of regulations for the principles of conducting such procedures in particular domains of public, social and economic affairs. The subject of this article is a detailed description of the stages of the procedure for the competitive selection of management staff. Good legislation to regulate the selection procedures for competitions for management appointments will have a significant influence on the effectiveness of the adopted method of selection. The author presents the successive stages of the procedures for such competitions and conducts an in-depth legal analysis, paying specific attention to legal doubts arising in connection with the application of the current law. On the basis of his analysis he formulates some proposals de lege ferenda addressed to the legislator on the introduction of requisite amendments and supplements to the legal regulations for the procedure of competitive selection of management staff.

scholarly journals On the Use of Information Criteria for Model Selection in Phylogenetics

2019 ◽  
Vol 37 (2) ◽  
pp. 549-562 ◽  
Author(s):  
Edward Susko ◽  
Andrew J Roger
Keyword(s):  
Model Selection ◽  
Bayes Factor ◽  
Information Criterion ◽  
Information Criteria ◽  
Leibler Divergence ◽  
Incorrect Model ◽  
Substantial Bias ◽  
And Performance ◽  
Phylogenetic Models ◽  
Selection Of

Abstract The information criteria Akaike information criterion (AIC), AICc, and Bayesian information criterion (BIC) are widely used for model selection in phylogenetics, however, their theoretical justification and performance have not been carefully examined in this setting. Here, we investigate these methods under simple and complex phylogenetic models. We show that AIC can give a biased estimate of its intended target, the expected predictive log likelihood (EPLnL) or, equivalently, expected Kullback–Leibler divergence between the estimated model and the true distribution for the data. Reasons for bias include commonly occurring issues such as small edge-lengths or, in mixture models, small weights. The use of partitioned models is another issue that can cause problems with information criteria. We show that for partitioned models, a different BIC correction is required for it to be a valid approximation to a Bayes factor. The commonly used AICc correction is not clearly defined in partitioned models and can actually create a substantial bias when the number of parameters gets large as is the case with larger trees and partitioned models. Bias-corrected cross-validation corrections are shown to provide better approximations to EPLnL than AIC. We also illustrate how EPLnL, the estimation target of AIC, can sometimes favor an incorrect model and give reasons for why selection of incorrectly under-partitioned models might be desirable in partitioned model settings.

Improvement of olfactometric measurement accuracy and repeatability by optimization of panel selection procedures

2010 ◽  
Vol 61 (5) ◽  
pp. 1267-1278 ◽  
Author(s):  
L. Capelli ◽  
S. Sironi ◽  
R. Del Rosso ◽  
P. Céntola ◽  
S. Bonati
Keyword(s):  
Degrees Of Freedom ◽  
Selection Procedure ◽  
Selection Procedures ◽  
Accuracy And Precision ◽  
Repeated Analysis ◽  
The Right ◽  
Accuracy And Repeatability ◽  
Selection Of ◽  
Dynamic Olfactometry

The EN 13725:2003, which standardizes the determination of odour concentration by dynamic olfactometry, fixes the limits for panel selection in terms of individual threshold towards a reference gas (n-butanol in nitrogen) and of standard deviation of the responses. Nonetheless, laboratories have some degrees of freedom in developing their own procedures for panel selection and evaluation. Most Italian olfactometric laboratories use a similar procedure for panel selection, based on the repeated analysis of samples of n-butanol at a concentration of 60 ppm. The first part of this study demonstrates that this procedure may originate a sort of “smartening” of the assessors, which means that they become able to guess the right answers in order to maintain their qualification as panel members, independently from their real olfactory perception. For this reason, the panel selection procedure has been revised with the aim of making it less repetitive, therefore preventing the possibility for panel members to be able to guess the best answers in order to comply with the selection criteria. The selection of new panel members and the screening of the active ones according to this revised procedure proved this new procedure to be more selective than the “standard” one. Finally, the results of the tests with n-butanol conducted after the introduction of the revised procedure for panel selection and regular verification showed an effective improvement of the laboratory measurement performances in terms of accuracy and precision.

How to obtain valid tests and confidence intervals after propensity score variable selection?

2019 ◽  
Vol 29 (3) ◽  
pp. 677-694 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Confidence Intervals ◽  
Selection Procedure ◽  
Routine Practice ◽  
Finite Sample ◽  
Major Drawback ◽  
Sample Distribution ◽  
Selection Procedures ◽  
Selection Of

The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure or treatment effects in observational studies. Routine practice is often based on stepwise selection procedures that use hypothesis testing, change-in-estimate assessments or the lasso, which have all been criticised for – amongst other things – not giving sufficient priority to the selection of confounders. This has prompted vigorous recent activity in developing procedures that prioritise the selection of confounders, while preventing the selection of so-called instrumental variables that are associated with exposure, but not outcome (after adjustment for the exposure). A major drawback of all these procedures is that there is no finite sample size at which they are guaranteed to deliver treatment effect estimators and associated confidence intervals with adequate performance. This is the result of the estimator jumping back and forth between different selected models, and standard confidence intervals ignoring the resulting model selection uncertainty. In this paper, we will develop insight into this by evaluating the finite-sample distribution of the exposure effect estimator in linear regression, under a number of the aforementioned confounder selection procedures. We will show that by making clever use of propensity scores, a simple and generic solution is obtained in the context of generalized linear models, which overcomes this concern (under weaker conditions than competing proposals). Specifically, we propose to use separate regularized regressions for the outcome and propensity score models in order to construct a doubly robust ‘g-estimator’; when these models are sufficiently sparse and correctly specified, standard confidence intervals for the g-estimator implicitly incorporate the uncertainty induced by the variable selection procedure.

Flexible Modelling of the Activated Sludge System – Theoretical and Practical Aspects

1985 ◽  
Vol 17 (2-3) ◽  
pp. 247-258 ◽  
Author(s):  
M. S. Sheffer ◽  
M. Hiraoka ◽  
K. Tsumura
Keyword(s):  
Model Selection ◽  
Computer Program ◽  
Time Domain ◽  
Mechanistic Models ◽  
Parameter Fitting ◽  
Modelling Method ◽  
Using Data ◽  
The Time Domain ◽  
Fitting In ◽  
Selection Of

For the purpose of optimal modelling, a “Flexible Modelling” method was developed. A flexible set of models consisting of hierarchical mechanistic models derived from a highly detailed structured model by mechanistic simplification was obtained. The performance of a computer program with an algorithm for parameter fitting in the time domain was evaluated by use of simulation. The program was able to estimate the models' parameters, even when using data with different degrees of inaccuracy. A computer program for model selection was developed, whereby the model was selected according to the information required. It was found that for prediction of the dynamic behavior of the MLVSS, the simplest model can supply all the necessary information. For prediction of effluent substrate concentration, the differences between the models' predictions depend on the characteristics of the disturbances and on the values of the models' parameters. The selection of the proper model and updating its parameters can be done by a computer which uses the presented program for model selection and parameter fitting.

Judicial Merit Selection: Beliefs about Fairness and the Undermining of Gender Diversity on the Bench

2020 ◽  
pp. 106591292097171
Author(s):  
Nancy B. Arrington
Keyword(s):  
Experimental Evidence ◽  
Institutional Design ◽  
A Priori ◽  
Gender Diversity ◽  
Selection Procedure ◽  
Procedural Fairness ◽  
Unintended Consequences ◽  
Selection Procedures ◽  
Judicial Selection ◽  
Selection Of

Much attention is paid to how mechanisms for selecting political officials shape which types of officials hold positions of power, but selection procedures do not always produce the desired outcomes. In the context of the judiciary, many expected “merit” selection procedures to facilitate the selection of women justices to the bench, an expectation that has not been realized. Applying theories of procedural fairness to judicial selection procedures, I argue that observers’ beliefs that merit selection procedures are more “fair” (relative to unilateral selection procedures) makes observers more accepting of all-male benches. Survey experimental evidence demonstrates that respondents do perceive merit selection procedures as more fair than gubernatorial selection procedures, a priori. In turn, respondents are less critical of all-male courts when judges are selected through a merit selection procedure. These findings contribute to our understanding of the ways in which (1) selection institutions shape prospects for gender diversity, (2) institutional design can have unintended consequences, and (3) procedural fairness can obscure accountabilituy for suboptimal outcomes.

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