scholarly journals Uso de Análisis de Covarianza (ANCOVA) en investigación científica

2017 ◽  
Vol 5 (9) ◽  
Author(s):  
M. H. Badii ◽  
J. Castillo ◽  
A. Wong
Keyword(s):  
Key Words ◽  
Real Data ◽  
Auxiliary Variable ◽  
Error Reduction ◽  
Error Variable ◽  
Variable Error

Key words: ANCOVA, auxiliary variable, error reduction, statisticsAbstract. The basics of the ANalisis of COVAriance (ANCOVA) are given. The objectives and the application of ANCOVA are laid out. Techniques for the estimation of contrasts and for the control and reduction of the degree of error are discussed. The application of a simple ANCOVA using real data is highlighted. The application of this technique in fixing the auxiliary variable in experimentation is emphasized.Palabras clave: ANCOVA, Estadística, reducción de error, variable auxiliarResumen. Se presentan las bases del ANálisis de COVArianza (ANCOVA). Se manejan los propósitos y la aplicación de este método estadístico. Se discuten las técnicas para la estimación de los contrastes, el control y la disminución del grado de error. Se presentan un ANCOVA simple mediante un ejemplo de datos reales. Se enfatiza el papel de esta técnica estadística en fijar el efecto de la variable auxiliar en el experimento.

scholarly journals Regression Estimator Using Double Ranked Set Sampling

2002 ◽  
Vol 7 (2) ◽  
pp. 311
Author(s):  
Hani M. Samawi ◽  
Eman M. Tawalbeh
Keyword(s):  
Correlation Coefficient ◽  
Real Data ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Regression Estimator ◽  
Primary Analysis ◽  
Data Set ◽  
Population Mean ◽  
The Mean

The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS) or ranked set sampling (RSS) (Yu and Lam, 1997) regression estimator.  Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4) and DRSS for high correlation coefficient (at least 0.91.) The theory is illustrated using a real data set of trees.  

scholarly journals Joint influence of measurement errors and randomized response technique on mean estimation under stratified double sampling

2021 ◽  
Vol 5 (1) ◽  
pp. 192-199
Author(s):  
Ronald Onyango ◽  
◽  
Brian Oduor ◽  
Francis Odundo ◽  
◽  
...  
Keyword(s):  
Measurement Errors ◽  
Mean Squared Error ◽  
Numerical Study ◽  
Real Data ◽  
Auxiliary Variable ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Joint Influence

The present study proposes a generalized mean estimator for a sensitive variable using a non-sensitive auxiliary variable in the presence of measurement errors based on the Randomized Response Technique (RRT). Expressions for the bias and mean squared error for the proposed estimator are correctly derived up to the first order of approximation. Furthermore, the optimum conditions and minimum mean squared error for the proposed estimator are determined. The efficiency of the proposed estimator is studied both theoretically and numerically using simulated and real data sets. The numerical study reveals that the use of the Randomized Response Technique (RRT) in a survey contaminated with measurement errors increases the variances and mean squared errors of estimators of the finite population mean.

scholarly journals Estimation of Finite Population Ratio When Other Auxiliary Variables are Available in the Study

2014 ◽  
Vol 44 (1) ◽  
pp. 33-46
Author(s):  
Jehad Al-Jararha ◽  
Ala' Bataineh
Keyword(s):  
Finite Population ◽  
Real Data ◽  
Auxiliary Variable ◽  
Data Set ◽  
Population Total ◽  
The Mean ◽  
Bias Variance ◽  
Highly Correlated ◽  
Population Ratio ◽  
Definition Of

The estimation of the population total $t_y,$ by using one or moreauxiliary variables, and the population ratio $\theta_{xy}=t_y/t_x,$$t_x$ is the population total for the auxiliary variable $X$, for afinite population are heavily discussed in the literature. In thispaper, the idea of estimation the finite population ratio$\theta_{xy}$ is extended to use the availability of auxiliaryvariable $Z$ in the study, such auxiliary variable  is not used inthe definition of the population ratio. This idea may be  supported by the fact that the variable $Z$  is highly correlated with the interest variable $Y$ than the correlation between the variables $X$ and $Y.$ The availability of such auxiliary variable can be used to improve the precision of the estimation of the population ratio.  To our knowledge, this idea is not discussed in the literature.  The bias, variance and the mean squares error  are given for our approach. Simulation from real data set,  the empirical relative bias and  the empirical relative mean squares error are computed for our approach and different estimators proposed in the literature  for estimating the population ratio $\theta_{xy}.$ Analytically and the simulation results show that, by suitable choices, our approach gives negligible bias and has less mean squares error.  

scholarly journals An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 281
Author(s):  
Shinpei Imori ◽  
Hidetoshi Shimodaira
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Real Data ◽  
Information Criterion ◽  
Auxiliary Variable ◽  
Parametric Model ◽  
Estimation Accuracy ◽  
Auxiliary Variables ◽  
Leibler Divergence ◽  
Leave One Out

Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of primary and auxiliary variables, it is possible to improve the estimation of parametric model for the primary variables when the auxiliary variables are closely related to the primary variables. However, the estimation accuracy reduces when the auxiliary variables are irrelevant to the primary variables. For selecting useful auxiliary variables, we formulate the problem as model selection, and propose an information criterion for predicting primary variables by leveraging auxiliary variables. The proposed information criterion is an asymptotically unbiased estimator of the Kullback–Leibler divergence for complete data of primary variables under some reasonable conditions. We also clarify an asymptotic equivalence between the proposed information criterion and a variant of leave-one-out cross validation. Performance of our method is demonstrated via a simulation study and a real data example.

Dynamic Timetabling Using Reactive Constraint Agents

2012 ◽  
Author(s):  
Hany Alashwal ◽  
Safaai Deris
Keyword(s):  
Key Words ◽  
Real Time ◽  
Real World ◽  
Software Agents ◽  
Real Data ◽  
Agent Architecture ◽  
Open Problems ◽  
Timetabling Problem ◽  
Programming Software ◽  
Minimal Modification

Kebanyakan teknik yang diimplimentasi bagi menyelesaikan masalah penjadualan tertumpu kepada proses yang statik. Walau bagaimanapun, di dalam dunia sebenar, masalah penjadualan merupakan satu masalah yang terbuka, dinamik dan sentiasa berubah–ubah mengikut kekangan dan andaian. Oleh yang demikian, objektif utama kertas ini adalah untuk mengendalikan perubahanperubahan yang berlaku setelah jadual waktu awalan terhasil. Agen Kekangan Reaktif (AKR) telah diimplimentasi lebih khusus dan berkeupayaan membaiki dan mengubahsuai jadual waktu secara bertahap dengan komunikasi dan kerjasama di antara satu sama lain bagi mengekalkan kesauran jadual waktu tersebut. Seni bina AKR ini telah dilaksana dan diuji dengan menggunakan data sebenar iaitu data dari Fakulti Sains, Universiti Ibb, Yemen. Hasil kajian menunjukkan bahawa AKR berupaya mengendalikan perubahan–perubahan dalam masa nyata dengan pembaikan yang minimum ke atas jadual waktu asal. Kata kunci: Masalah penjadualan waktu, penjadualan waktu dinamik, pengaturcaraan terhad, agen perisian, seni bina agen terbuka Most of the approaches that have been applied to solve the timetabling problems focus on the construction of the timetable as a static process. In real world, the timetabling problems are dynamic and open problems since the initial timetable is not fixed and it is required to be changed as the constraints or assumptions on which the timetable is based on, are changed or became invalid. Therefore, the main objective of this paper is to handle the changes after generating the initial timetable. The Reactive Constraint Agents (RCA) architecture is capable of repairing and modifying the timetable gradually by communicating and cooperating with each other to maintain the timetable feasibility. This architecture has been implemented and tested using real data from Faculty of Science, University of Ibb – Yemen. The results show that the RCA can cope with the changes in real–time with minimal modification to the existing timetable. Key words: Timetabling problem, dynamic timetabling, constraints programming, software agents, open agent architecture

scholarly journals Quantification of Uncertainties in OCO-2 Measurements of XCO<sub>2</sub>: Simulations and Linear Error Analysis

2016 ◽  
Author(s):  
Brian Connor ◽  
Hartmut Boesch ◽  
James McDuffie ◽  
Tommy Taylor ◽  
Dejian Fu ◽  
...  
Keyword(s):  
Bias Correction ◽  
Model Parameters ◽  
Error Sources ◽  
Dry Air ◽  
Error Variable ◽  
Variable Error ◽  
Operational Algorithm ◽  
The Difference ◽  
Variable Errors ◽  
Types Of Error

Abstract. We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 ('XCO2') by the NASA Orbiting Carbon Observatory-2, ('OCO-2'). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the 'variable error' which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Variable errors are usually

scholarly journals A Ratio Estimator Under General Sampling Design

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Jehad Al-Jararha ◽  
Mohammed Al-Haj Ebrahem
Keyword(s):  
Coefficient Of Variation ◽  
Sampling Design ◽  
Real Data ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Alternative Form ◽  
Ratio Estimator ◽  
Data Set ◽  
Population Ratio ◽  
Better Than

Recently, many authors introduced ratio-type estimators for estimating the mean, or the ratio, for a finite populations. Most of the articles are discussing this problem under simple random sampling design, with more assumptions on the auxiliary variable such as the coefficient of variation, and kurtosis are assumed to be known. Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimators and they assumed the coefficient of variation of the auxiliary variable must be known; this assumption is crucialfor this estimator.An estimator of the population ratio, under general sampling design, is proposed.Further, exact and an unbiased variance estimator of this estimator are obtained, and the Godambe-Joshi lower bound is asymptotically attainable for this estimator. The assumption on the coefficient of variation of the auxiliary variable is not needed for the proposed estimator. Simulation results from real data set and simulations from artificial population, show that the performance of the proposed estimator is better than Gupta and Shabbir (2008) and Hartley and Ross (1954) estimators.

Encultured minds, not error reduction minds

2020 ◽  
Vol 43 ◽  
Author(s):  
Robert Mirski ◽  
Mark H. Bickhard ◽  
David Eck ◽  
Arkadiusz Gut
Keyword(s):  
Free Energy ◽  
Error Reduction ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Current Article

Abstract There are serious theoretical problems with the free-energy principle model, which are shown in the current article. We discuss the proposed model's inability to account for culturally emergent normativities, and point out the foundational issues that we claim this inability stems from.

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

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