scholarly journals Kernel Equating Using Propensity Scores for Nonequivalent Groups

2019 ◽  
Vol 44 (4) ◽  
pp. 390-414 ◽  
Author(s):  
Gabriel Wallin ◽  
Marie Wiberg
Keyword(s):  
Propensity Score ◽  
Test Scores ◽  
Propensity Scores ◽  
Mean Squared Error ◽  
Squared Error ◽  
Admissions Test ◽  
Nonequivalent Groups ◽  
Kernel Equating ◽  
Common Items ◽  
Practical Implications

When equating two test forms, the equated scores will be biased if the test groups differ in ability. To adjust for the ability imbalance between nonequivalent groups, a set of common items is often used. When no common items are available, it has been suggested to use covariates correlated with the test scores instead. In this article, we reduce the covariates to a propensity score and equate the test forms with respect to this score. The propensity score is incorporated within the kernel equating framework using poststratification and chained equating. The methods are evaluated using real college admissions test data and through a simulation study. The results show that propensity scores give an increased equating precision in comparison with the equivalent groups design and a smaller mean squared error than by using the covariates directly. Practical implications are also discussed.

scholarly journals Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Bottigliengo ◽  
Ileana Baldi ◽  
Corrado Lanera ◽  
Giulia Lorenzoni ◽  
Jonida Bejko ◽  
...  
Keyword(s):  
Propensity Score ◽  
Sample Size ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Squared Error ◽  
Matching Procedure ◽  
Nominal Coverage

Abstract Background Propensity score matching is a statistical method that is often used to make inferences on the treatment effects in observational studies. In recent years, there has been widespread use of the technique in the cardiothoracic surgery literature to evaluate to potential benefits of new surgical therapies or procedures. However, the small sample size and the strong dependence of the treatment assignment on the baseline covariates that often characterize these studies make such an evaluation challenging from a statistical point of view. In such settings, the use of propensity score matching in combination with oversampling and replacement may provide a solution to these issues by increasing the initial sample size of the study and thus improving the statistical power that is needed to detect the effect of interest. In this study, we review the use of propensity score matching in combination with oversampling and replacement in small sample size settings. Methods We performed a series of Monte Carlo simulations to evaluate how the sample size, the proportion of treated, and the assignment mechanism affect the performances of the proposed approaches. We assessed the performances with overall balance, relative bias, root mean squared error and nominal coverage. Moreover, we illustrate the methods using a real case study from the cardiac surgery literature. Results Matching without replacement produced estimates with lower bias and better nominal coverage than matching with replacement when 1:1 matching was considered. In contrast to that, matching with replacement showed better balance, relative bias, and root mean squared error than matching without replacement for increasing levels of oversampling. The best nominal coverage was obtained by using the estimator that accounts for uncertainty in the matching procedure on sets of units obtained after matching with replacement. Conclusions The use of replacement provides the most reliable treatment effect estimates and that no more than 1 or 2 units from the control group should be matched to each treated observation. Moreover, the variance estimator that accounts for the uncertainty in the matching procedure should be used to estimate the treatment effect.

Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

2008 ◽  
Vol 24 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Niko Kohls ◽  
Harald Walach
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Repeated Measures ◽  
Propensity Scores ◽  
Total Sample ◽  
Experimental Manipulation ◽  
Brief Symptom Inventory ◽  
Time Interaction ◽  
Social Support Scale ◽  
Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Thematic Maps for the Variation of Bearing Capacity of Soil Using SPTs and MATLAB

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 329
Author(s):  
Mahdi O. Karkush ◽  
Mahmood D. Ahmed ◽  
Ammar Abdul-Hassan Sheikha ◽  
Ayad Al-Rumaithi
Keyword(s):  
Bearing Capacity ◽  
Mean Squared Error ◽  
Ground Level ◽  
The Other ◽  
Thematic Maps ◽  
First Order ◽  
Squared Error ◽  
Order Polynomial ◽  
Interpolation Polynomials ◽  
Penetration Tests

The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production of thematic maps showing the variation of the bearing capacity of the soil over the whole area of Al-Basrah city correlated with several depths. The bearing capacity of soil obtained from the suggested first-order polynomial matches well with those calculated from the results of SPTs with a deviation of ±30% at a 95% confidence interval.

scholarly journals The Effects of Employment on Depression and Life Satisfaction Among Old-Aged Using the DD Method Combined With PSM

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 408-408
Author(s):  
Si Young Song ◽  
Hey Jung Jun ◽  
Sun Ah Lee
Keyword(s):  
Life Satisfaction ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Multiple Regression ◽  
Propensity Scores ◽  
Panel Study ◽  
Double Difference ◽  
Combined Model ◽  
Comparative Group ◽  
Experimental Group

Abstract The purpose of this study is to explore the effect of employment on depression and life satisfaction among old-aged. Using 12th (2017) wave and 13th (2018) wave of Korean Welfare Panel Study (KoWePS), three stages of analyses were conducted. First, through propensity score matching (PSM) method, sample with similar propensity scores was matched between the group that did not work in 12th wave but worked in 13th wave (experimental group, N=180), and the group that did not work in 12th and 13th wave (comparative group, N=180). Second, the matched sample was used to conduct multiple regression analysis with the group dummy variable (experimental group, comparative group) as an independent variable, and depression and life satisfaction as the dependent variables. Third, combined model of propensity score matching (PSM) and double difference (DD) method was conducted to more appropriately derive the net effect of employment. The results of multiple regression after propensity matching showed that employment had a positive effect on reducing depression (B= -1.70, p< .01) and increasing life satisfaction (B= .12, p< .01) in old-aged. Furthermore, in combined model of PSM and DD, life satisfaction was improved when employed compared to non-employed (B= .15, p< .05). The results of this study are meaningful in that the meaning of employment in old-aged is more clearly derived by solving selection bias and endogenous problems. Also, this study may provide reference for establishing welfare policies related to employment among old-aged.

scholarly journals Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1237
Author(s):  
Ivan Pisa ◽  
Antoni Morell ◽  
Ramón Vilanova ◽  
Jose Lopez Vicario
Keyword(s):  
Mean Squared Error ◽  
Control Strategies ◽  
Treatment Plant ◽  
Industrial Control ◽  
Output Data ◽  
Complex Processes ◽  
Squared Error ◽  
Complex Design ◽  
Solution Design ◽  
Delay Correction

Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

Sign in / Sign up

Export Citation Format

Share Document