The Probability Prediction of Mobile Coupons’ Offline Use Based on Copula-MC Method

LISS2019 ◽  
2020 ◽  
pp. 123-137
Author(s):  
Yue Chen ◽  
Yisong Li
Keyword(s):  
Probability Prediction ◽  
Mobile Coupons ◽  
Mc Method

Day-ahead Load Probabilistic Forecasting Based on Space-time Correction

2020 ◽  
Vol 13 ◽  
Author(s):  
Fei Jin ◽  
Xiaoliang Liu ◽  
Fangfang Xing ◽  
Guoqiang Wen ◽  
Shuangkun Wang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Northern China ◽  
Space Time ◽  
Load Prediction ◽  
Probabilistic Forecasting ◽  
Residual Distribution ◽  
Time And Space ◽  
Probability Prediction ◽  
Time Correction ◽  
Interval Coverage

Background : The day-ahead load forecasting is an essential guideline for power generating, and it is of considerable significance in power dispatch. Objective: Most of the existing load probability prediction methods use historical data to predict a single area, and rarely use the correlation of load time and space to improve the accuracy of load prediction. Methods: This paper presents a method for day-ahead load probability prediction based on space-time correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction error of the long short-term memory (LSTM) model, and the residual distribution is obtained. Then the correlation value is used to modify the time and space dimensions of the test set's partial period prediction values. Results: The experiment selected three years of load data in 10 areas of a city in northern China. The MAPE of the two modified models on their respective test sets can be reduced by an average of 10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction can be increased by an average of 4.2% and 1.8% than before. Conclusion: The test results show that the proposed correction schemes are feasible.

QMC integration errors and quasi-asymptotics

2020 ◽  
Vol 26 (3) ◽  
pp. 171-176
Author(s):  
Ilya M. Sobol ◽  
Boris V. Shukhman
Keyword(s):  
Monte Carlo ◽  
Error Estimate ◽  
Numerical Experiments ◽  
Probable Error ◽  
Quasi Monte Carlo ◽  
Integration Error ◽  
Asymptotic Error ◽  
Dimensional Cube ◽  
Integration Errors ◽  
Mc Method

AbstractA crude Monte Carlo (MC) method allows to calculate integrals over a d-dimensional cube. As the number N of integration nodes becomes large, the rate of probable error of the MC method decreases as {O(1/\sqrt{N})}. The use of quasi-random points instead of random points in the MC algorithm converts it to the quasi-Monte Carlo (QMC) method. The asymptotic error estimate of QMC integration of d-dimensional functions contains a multiplier {1/N}. However, the multiplier {(\ln N)^{d}} is also a part of the error estimate, which makes it virtually useless. We have proved that, in the general case, the QMC error estimate is not limited to the factor {1/N}. However, our numerical experiments show that using quasi-random points of Sobol sequences with {N=2^{m}} with natural m makes the integration error approximately proportional to {1/N}. In our numerical experiments, {d\leq 15}, and we used {N\leq 2^{40}} points generated by the SOBOLSEQ16384 code published in 2011. In this code, {d\leq 2^{14}} and {N\leq 2^{63}}.

scholarly journals IMG-14. DEVELOPING A PREDICTIVE GRADING MODEL FOR CHILDREN WITH GLIOMAS BASED ON DIFFUSION KURTOSIS IMAGING METRICS: ACCURACY AND CLINICAL CORRELATIONS WITH SURVIVAL

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii357-iii358
Author(s):  
Ioan Paul Voicu ◽  
Antonio Napolitano ◽  
Alessia Carboni ◽  
Lorenzo Lattavo ◽  
Andrea Carai ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
High Grade Glioma ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Free Survival ◽  
Kaplan Meier ◽  
Model Predictions ◽  
Probability Prediction ◽  
Grading Model ◽  
Diffusion Kurtosis

Abstract PURPOSE To develop a predictive grading model based on diffusion kurtosis imaging (DKI) metrics in children affected by gliomas, and to investigate the clinical impact of the model via correlations with overall survival and progression-free survival. MATERIALS AND METHODS We retrospectively studied 59 children (33M, 26F, median age 7.2 years) affected by gliomas on a 3T magnet. Patients with tumor locations other than infratentorial midline were included. Conventional and DKI sequences were obtained. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were obtained. Whole tumor volumes (VOIs) were segmented semiautomatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model with penalized logistic regression (glmnet package, R). Elasticnet regularization was used to avoid model overfitting. Fitted model coefficients from each metric were used to develop a probability prediction of a high-grade glioma (HGG). Grading accuracy of the resulting probabilities was tested with ROC analysis. Finally, model predictions were correlated to progression-free survival (PFS) with a Kaplan-Meier analysis. RESULTS The cohort included 46 patients with low-grade gliomas (LGG) and 13 patients with HGG. The developed model predictions yielded an AUC of 0.946 (95%CI: 0.890–1). Model predictions were significantly correlated with PFS (23.1 months for HGG vs 34.7 months for LGG, p<0.004). CONCLUSION In our cohort, a DKI-based predictive model was highly accurate for pediatric glioma grading. DKI-based model predictions were significantly correlated with progression-free survival.

scholarly journals Probability of Roadside Accidents for Curved Sections on Highways

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Guozhu Cheng ◽  
Rui Cheng ◽  
Yulong Pei ◽  
Liang Xu
Keyword(s):  
Risk Factors ◽  
Decision Rules ◽  
Significant Risk ◽  
Vehicle Speed ◽  
Probabilistic Prediction ◽  
Interaction Detection ◽  
Probability Prediction ◽  
Chi Squared ◽  
Longitudinal Slope ◽  
Curve Radius

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

scholarly journals Bayesian reassessment of the epigenetic architecture of complex traits

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Trejo Banos ◽  
Daniel L. McCartney ◽  
Marion Patxot ◽  
Lucas Anchieri ◽  
Thomas Battram ◽  
...  
Keyword(s):  
Complex Traits ◽  
Complex Disease ◽  
Target Identification ◽  
Blood Cholesterol ◽  
Methylation Array ◽  
Single Nucleotide ◽  
Epigenetic Effects ◽  
Probability Prediction ◽  
Stimuli Response ◽  
Insight Into

Abstract Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

Nephrometric score based on 3D modeling (3D nephrometry score) for the probability prediction of intra- and postoperative complications for kidney surgery

2021 ◽  
pp. 039156032110364
Author(s):  
Georgiy Andreevich Mashin ◽  
Vasiliy Vladimirovich Kozlov ◽  
Denis Vladimirovich Chinenov ◽  
Yaroslav Nikolaevich Chernov ◽  
Alexandra Vladimirovna Proskura ◽  
...  
Keyword(s):  
Postoperative Complications ◽  
Blood Loss ◽  
3D Modeling ◽  
Roc Analysis ◽  
Linear Models ◽  
Surgical Intervention ◽  
Renal Tumors ◽  
Discriminant Functions ◽  
Nephrometry Score ◽  
Probability Prediction

Aim: The purpose of the study is the development and evaluation of the informativeness of the author’s 3D nephrometric score application to predict the probability of intraoperative and postoperative complications in kidney operations. Material and methods: The study includes 264 patients who underwent surgical treatment of renal tumors, before that CT and 3D modeling were carried out. All patients underwent an analysis of the surgical intervention complexity on the C-index, PADUA, R.E.N.A.L., and developed 3D nephrometric score. To determine the set of variables that allow to classify patients, the method of discriminant analysis was used to predict the nature, volume of blood loss, duration of ischemia, and the number of complications. The sensitivity and specificity of the predictors were estimated with the help of ROC analysis. Results: Indicators have been established to classify patients according to the probability of complications, the amount of blood loss and the duration of ischemia during surgery for kidney cancer. We have created linear models that predict the development of bleeding during surgery, the volume of blood loss of more than 200 ml and the duration of ischemia more than 20 min, as well as the likelihood of complications using discriminant functions. The proposed author’s nephrometric score exceeds the capabilities of C-index, PADUA, R.E.N.A.L in many ways in blood loss and time of ischemia predicting, which allows us to recommend it for the assessment of resectability in kidney operations.

Consumers’ redemption behavior of recommended mobile coupons in social network sites

2019 ◽  
Vol 57 (9) ◽  
pp. 2477-2500 ◽  
Author(s):  
Qing Tang ◽  
Fen Liu ◽  
Shan Liu ◽  
Yunfeng Ma
Keyword(s):  
Social Network ◽  
Perceived Risk ◽  
Social Network Sites ◽  
Economic Benefits ◽  
Social Recommendation ◽  
Content Type ◽  
Coupon Redemption ◽  
Mobile Coupons ◽  
Key Drivers ◽  
Trust Propensity

Purpose The purpose of this paper is to explore the key factors that affect consumer redemption intention toward mobile coupons recommended in social network sites (SNS). Design/methodology/approach A research model that integrates recommendation trust, positive utilities, and negative utilities of coupon redemption is developed. With the important role of trust in social recommendation taken into consideration, the key drivers of recommendation trust were analyzed in the model. Data were collected from 210 users with mobile coupon recommendation experience in one of the largest SNS (i.e. WeChat) in China. The authors used partial least squares technique to analyze the model. Findings Recommendation trust and positive utilities (economic benefits and perceived enjoyment) positively affect the intention of mobile coupon redemption. Perceived risk, as a negative utility, negatively influences coupon redemption intention. In addition, swift trust (structure assurance, perceived similarity, trust propensity, and expertise of the recommender), knowledge-based trust (familiarity with the retailers), and emotion-based trust (social tie strength) are key drivers that promote recommendation trust. Originality/value While prior research investigated mobile coupon redemption behavior in which coupons were issued by merchants, limited research analyzed consumer responses toward mobile coupons in social recommendation. This study examines the effects of recommendation trust, positive utilities, and negative utilities on mobile coupon redemption in the context of social recommendation and recognizes the key drivers of recommendation trust.

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