scholarly journals Predictive Power in Behavioral Welfare Economics

2020 ◽  
Author(s):  
Elias Bouacida ◽  
Daniel Martin
Keyword(s):  
Predictive Power ◽  
Welfare Analysis ◽  
Welfare Economics ◽  
Revealed Preferences ◽  
Data Sets ◽  
Behavioral Biases ◽  
Standard Data ◽  
Model Free ◽  
High Predictive Power ◽  
Choice Set

Abstract When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether the structure of a model is necessary for providing precise welfare guidance based on those choices. To address this question empirically, we use standard data sets from the lab and field to evaluate the predictive power of two “model-free” approaches to behavioral welfare analysis. We find they typically have high predictive power, which means there is little ambiguity about what should be selected from each choice set. We also identify properties of revealed preferences that help to explain the predictive power of these approaches.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

Focusing near Infrared Spectroscopy on the Business Objectives of Modern Pharmaceutical Production

1998 ◽  
Vol 6 (A) ◽  
pp. A13-A19 ◽  
Author(s):  
T.G. Axon ◽  
R. Brown ◽  
S.V. Hammond ◽  
S.J. Maris ◽  
F. Ting
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Develop Model ◽  
Data Sets ◽  
Total Quality ◽  
Model Free ◽  
Pharmaceutical Production

The early use of near infrared (NIR) spectroscopy in the pharmaceutical industry was for raw material identification, later moving on to some conventional “calibrations” for various ingredients in a variety of sample types. The approach throughout this development process has always been “conventional” with one measurement by NIR directly replacing some other slower method, be it Mid-IR identification, or determinations by Karl Fischer, high performance liquid chromatography (HPLC)etc. A significant change in approach was demonstrated by Plugge and Van der Vlies1 in 1993, where a qualitative system was used to provide “quantitative like” answers for potency of a drug substance. Following on from that key paper, there has been a realisation that the qualitative analysis ability of NIR, has the potential to be a powerful tool for process investigation, control and validation. The final step has been to develop “model free” approaches, that consider individual data sets as unique systems, and present the opportunity for NIR to escape the shackles of “calibration” in one form or another. The use of qualitative, or model free, approaches to NIR spectroscopy provides an effective tool for satisfying many of the demands of modern pharmaceutical production. “Straight through production,” “right first time,” “short cycle time” and “total quality management” philosophies can be realised. Eventually the prospect of parametric release may be materialised with a strong contribution from NIR spectroscopy. This paper will illustrate the above points with some real life examles.

scholarly journals Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Wang ◽  
Yanni Li ◽  
Xiaoyi Wang ◽  
Ranko Gacesa ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Detection ◽  
Predictive Power ◽  
Disease Screening ◽  
Health Check ◽  
Initial Presentation ◽  
Screening Tools ◽  
Clinical Markers ◽  
Liver Disorders ◽  
High Predictive Power

Background. Early detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge. Aims. To analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection. Methods. We performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders. Results. Asymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%, p<0.001). The cirrhotic rates at an initial presentation are decreased in the past 18 years (from 52.6% to 20.0%, p<0.001). Eight indicators, which are common in the health check-up, are independent risk factors of AILD. Among them, abdominal lymph node enlargement (LN) positive is the most significant different (OR 8.85, 95% CI 2.73-28.69, p<0.001). The combination of these indicators shows high predictive power (AUC=0.98, sensitivity 89.0% and specificity 96.4%) for disease screening. Except two liver or cholangetic injury makers, the combination of AGE, GENDER, GLB, LN, concomitant extrahepatic autoimmune diseases, and familial history also shows a high predictive power for AILD in other liver disorders (AUC=0.91). Conclusion. Screening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.

scholarly journals Stochastic Revealed Preferences with Measurement Error

2020 ◽  
Author(s):  
Victor H Aguiar ◽  
Nail Kashaev
Keyword(s):  
International Economic ◽  
Measurement Error ◽  
Panel Data ◽  
Revealed Preference ◽  
Revealed Preferences ◽  
Data Sets ◽  
Single Individual ◽  
Data Set ◽  
International Economic Review ◽  
New Evidence

Abstract A long-standing question about consumer behaviour is whether individuals’ observed purchase decisions satisfy the revealed preference (RP) axioms of the utility maximization theory (UMT). Researchers using survey or experimental panel data sets on prices and consumption to answer this question face the well-known problem of measurement error. We show that ignoring measurement error in the RP approach may lead to overrejection of the UMT. To solve this problem, we propose a new statistical RP framework for consumption panel data sets that allows for testing the UMT in the presence of measurement error. Our test is applicable to all consumer models that can be characterized by their first-order conditions. Our approach is non-parametric, allows for unrestricted heterogeneity in preferences and requires only a centring condition on measurement error. We develop two applications that provide new evidence about the UMT. First, we find support in a survey data set for the dynamic and time-consistent UMT in single-individual households, in the presence of nonclassical measurement error in consumption. In the second application, we cannot reject the static UMT in a widely used experimental data set in which measurement error in prices is assumed to be the result of price misperception due to the experimental design. The first finding stands in contrast to the conclusions drawn from the deterministic RP test of Browning (1989, International Economic Review, 979–992). The second finding reverses the conclusions drawn from the deterministic RP test of Afriat (1967, International Economic Review, 8, 6–77) and Varian (1982, Econometrica, 945–973).

Development of a Powerful Data-Analysis Tool Using Nonparametric Smoothing Models To Identify Drillsites in Tight Shale Reservoirs With High Economic Potential

SPE Journal ◽  
2017 ◽  
Vol 23 (03) ◽  
pp. 719-736 ◽  
Author(s):  
Quan Cai ◽  
Wei Yu ◽  
Hwa Chi Liang ◽  
Jenn-Tai Liang ◽  
Suojin Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Predictive Power ◽  
Oil And Gas ◽  
Predictor Variables ◽  
Data Sets ◽  
Analysis Tool ◽  
Data Set ◽  
Data Analysis Tool ◽  
Shale Reservoirs

Summary The oil-and-gas industry is entering an era of “big data” because of the huge number of wells drilled with the rapid development of unconventional oil-and-gas reservoirs during the past decade. The massive amount of data generated presents a great opportunity for the industry to use data-analysis tools to help make informed decisions. The main challenge is the lack of the application of effective and efficient data-analysis tools to analyze and extract useful information for the decision-making process from the enormous amount of data available. In developing tight shale reservoirs, it is critical to have an optimal drilling strategy, thereby minimizing the risk of drilling in areas that would result in low-yield wells. The objective of this study is to develop an effective data-analysis tool capable of dealing with big and complicated data sets to identify hot zones in tight shale reservoirs with the potential to yield highly productive wells. The proposed tool is developed on the basis of nonparametric smoothing models, which are superior to the traditional multiple-linear-regression (MLR) models in both the predictive power and the ability to deal with nonlinear, higher-order variable interactions. This data-analysis tool is capable of handling one response variable and multiple predictor variables. To validate our tool, we used two real data sets—one with 249 tight oil horizontal wells from the Middle Bakken and the other with 2,064 shale gas horizontal wells from the Marcellus Shale. Results from the two case studies revealed that our tool not only can achieve much better predictive power than the traditional MLR models on identifying hot zones in the tight shale reservoirs but also can provide guidance on developing the optimal drilling and completion strategies (e.g., well length and depth, amount of proppant and water injected). By comparing results from the two data sets, we found that our tool can achieve model performance with the big data set (2,064 Marcellus wells) with only four predictor variables that is similar to that with the small data set (249 Bakken wells) with six predictor variables. This implies that, for big data sets, even with a limited number of available predictor variables, our tool can still be very effective in identifying hot zones that would yield highly productive wells. The data sets that we have access to in this study contain very limited completion, geological, and petrophysical information. Results from this study clearly demonstrated that the data-analysis tool is certainly powerful and flexible enough to take advantage of any additional engineering and geology data to allow the operators to gain insights on the impact of these factors on well performance.

Sign in / Sign up

Export Citation Format

Share Document