Bias-Correction of Regression Models: A Case Study on hERG Inhibition

2009 ◽  
Vol 49 (6) ◽  
pp. 1486-1496 ◽  
Author(s):  
Katja Hansen ◽  
Fabian Rathke ◽  
Timon Schroeter ◽  
Georg Rast ◽  
Thomas Fox ◽  
...  
Keyword(s):  
Bias Correction ◽  
Regression Models

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia

Risks ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Valentina Kravchenko ◽  
Tatiana Kudryavtseva ◽  
Yuriy Kuporov
Keyword(s):  
Regression Models ◽  
Public Procurement ◽  
Ordinary Least Squares ◽  
Economic Security ◽  
Government Procurement ◽  
Security Monitoring ◽  
Significant Relationships ◽  
Monitoring Tools ◽  
Russian Region

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.

scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

From Community to Public Familiarity: Neighborhood, Sociability, and Belonging in the Neoliberal City

2021 ◽  
pp. 107808742110215
Author(s):  
Felipe Link ◽  
Andrés Señoret ◽  
Felipe Valenzuela
Keyword(s):  
Social Relations ◽  
Sense Of Belonging ◽  
Regression Models ◽  
Secondary Data ◽  
Critical Factor ◽  
Urban Morphology ◽  
Demographic Characteristics ◽  
Logistic Regression Models ◽  
Pedestrian Space

Current urban neoliberalism processes have shaped and changed contemporary cities, including the local scale’s built environment and social relations. This article aims to study how such transformations affect local sociability by analyzing the effects of neighborhoods’ morphology and socio-demographic characteristics on different forms of interactions and how they affect the sense of belonging. Taking the Metropolitan Area of Santiago, Chile, as a case study, we gathered secondary data on urban morphology and surveyed ten neighborhoods to measure sociability patterns. The results obtained from multilevel logistic regression models show that time living in the neighborhood and public pedestrian space is the most critical factor affecting neighborhood sociability. Moreover, instead of local ties, public familiarity is the form of sociability with the most substantial effects on a sense of belonging. We conclude that recent neighborhoods, formed by neoliberal urbanization, tend to discourage neighborhood sociability and a sense of belonging.

scholarly journals Development Model on Prevention of Land and Forest Fire in the Peat Land Area with Empowerment Society Approach (Case Study in West Kalimantan)

2021 ◽  
Vol 3 (3) ◽  
pp. 185-195
Author(s):  
Maswadi ◽  
Shenny Oktoriana ◽  
Rini Hazriani ◽  
Maulidi
Keyword(s):  
Regression Models ◽  
Agricultural Extension ◽  
Change Model ◽  
Land Area ◽  
West Kalimantan ◽  
Household Level ◽  
Burning Behavior ◽  
The Social ◽  
Land Cultivation

Land fires are an annual agenda in Indonesia, especially in areas covered by peatlands. Peatland management using fire is the main cause. The general paradigm for land fires is that the farmers' economy is low. In fact, the factors that cause land fires are not only from the economic aspect, but from the social and institutional aspects which affect the behavior of farmers. Therefore, the purpose of this study is (1) to analyze the correlation of factors that influence land burning behavior, and (2) to determine the model for the changing of burning behavior into non burning farm land cultivation, in order to prevent land fire in Kuburaya Disrict and Bengkayang District, West Kalimantan. The research involved farmers farming on peatlands in both districts. Regression models was used for the correlation analysis. Based on the results of the regression analysis, a land burning behavior change model was determined based on the influencing factors descriptively. The result of the analysis showed the factors that significantly influence land burning behavior at the household level are knowledge level (consist of farming problems faced by farmers, agricultural extension materials and the application of technology), observance of rules, activeness of mutual assistance, cosmopolitan level, appraisal on burning behavior, and imitation level. The resulting model for changing the behavior of burning land is implementing activity of assistance, social capital stimulus and positive agreements/ pressure to the land users.

scholarly journals Modelling crop pattern changes and water resources exploitation: a case study

2017 ◽  
Author(s):  
Donato Zingaro ◽  
Ivan Portoghese ◽  
Giacomo Giannoccaro
Keyword(s):  
Water Resources ◽  
Regression Models ◽  
Policy Support ◽  
Market Prices ◽  
Environmental Threats ◽  
Land And Water Management ◽  
Irrigated Crops ◽  
Direct Payments ◽  
Resources Exploitation

Agriculture and farming worldwide are responsible for numerous environmental threats, including degradation of land and water resources depletion. Underlining the dynamic interaction between bio-physical and socio-economic drivers is the key towards a more sustainable land and water management. With regard to a highly developed agricultural area in southern Italy, multi-regression models were developed to interpret the observed inter-annual variability of cropped land. Main drivers related to Common Agricultural Policy support, product market prices, crop yield and irrigation water availability were investigated. The adopted models revealed the different weighs of each driver. The findings reported the role that direct payments played in supporting the extension of irrigated crops, such as processing tomato. Likewise, the models pointed to decoupled payment scheme as the most important driver of change in the crop pattern over the last years.

scholarly journals Socioeconomic Classes of University Students in Pakistan as Predictors of their Personality Traits: A Case Study

2021 ◽  
Vol 2 (2) ◽  
pp. 205-221
Author(s):  
Rafaqat Ali ◽  
Furrukh Bashir ◽  
Rashid Ahmad
Keyword(s):  
University Students ◽  
Personality Traits ◽  
Regression Models ◽  
Negative Impact ◽  
Positive Impact ◽  
Socioeconomic Class ◽  
Lower Socioeconomic ◽  
Negative Impacts ◽  
The Impact

The current study was heading for determining the impact of Pakistani university students’ socioeconomic classes on their personality traits. Demographic and personality questionnaires were filled by available university students online. The stepwise regression technique facilitated to generate regression models to define impacts of different socioeconomic classes on students’ different personality traits. Different regression models highlighted the significant negative impacts of the middle upper socioeconomic class on Agreeableness, Extraversion and Neuroticism personality traits. The lower socioeconomic class was found to have positive impact on only one personality sub-trait self-discipline. Whereas, the upper lower socioeconomic class caused positive impacts on students’ trust sub-trait, Conscientiousness trait and negative impact on excitement seeking sub-trait of personality. The importance of these impacts of socioeconomic classes on different personality traits and the possible implications are discussed with respect to university students’ academic performance and academic behaviour.

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