INFERRING THE ECONOMIC PREFERENCE OF A RENTAL VEHICLE COMPANY BY MODELING ITS DE-FLEETING PROCESS

2016 ◽  
Vol 11 (02) ◽  
pp. 1650006
Author(s):  
CHUAN-HSIANG HAN ◽  
JINGREN SHI ◽  
SUZHOU HUANG
Keyword(s):  
Decision Making ◽  
Optimal Stopping ◽  
Simulated Data ◽  
Real Data ◽  
Model Parameters ◽  
Market Behavior ◽  
Vehicle Manufacturer ◽  
Economic Preference ◽  
Optimal Stopping Theory ◽  
Technical Issues

When a vehicle manufacturer designs a contract with a rental vehicle company, it is important for the OEM to properly understand the rental company’s economic preference. While it is usually not directly observable, the economic preference of the counter party can often be revealed indirectly through some observable market behavior. In such cases, econometric inference needs to be used. In this paper, we use the de-fleeting process of the rental vehicle company as the inferential apparatus. To this end, we first develop a model to describe the decision-making in the de-fleeting process for the rental vehicle company, based on the optimal stopping theory. We then outline an econometric procedure to estimate the model parameters. Finally, we use simulated data to illustrate how to deal with some of the technical issues that one might encounter when the procedure is applied to real data.

scholarly journals Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xueqin Long ◽  
Liancai Zhang ◽  
Shanshan Liu ◽  
Jianjun Wang
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Real Data ◽  
Cumulative Prospect Theory ◽  
Reference Points ◽  
Model Parameters ◽  
Lane Changing ◽  
Actual Observation ◽  
The Difference ◽  
Lane Keeping

In this paper, the decision-making model of discretionary lane-changing is established using cumulative prospect theory (CPT). Through analyzing the vehicles’ dynamic running states, safety spacing calculating approaches for discretionary lane-changing and lane-keeping have been put forward firstly. Then, based on CPT, a lane-changing decision model with accelerating space as its utility is proposed by estimating the difference between actual spacings and the safety spacings for discretionary lane-changing as well as lane-keeping. In order to calculate the utility of discretionary lane-changing, dynamic reference points and a parameter representing driver’s risk preference are introduced into the model. With the real data collected from an urban expressway, the distribution of discretionary lane-changing duration is analyzed, and the model parameters are also calibrated. Furthermore, the applicability of the model is evaluated by comparing with the actual observation and random unity model. Finally, the sensitivity analysis of the model is carried out, that is, assessing the influence degree of each variable on the decision result. The study reveals that the CPT-based model can describe discretionary lane-changing behavior more accurately, which consider drivers’ risk-aversion during decision-making.

An approach to computer analysis of the ligand binding assay data on example of radioligand assay data

2020 ◽  
Vol 18 (02) ◽  
pp. 2050014
Author(s):  
S. N. Fedotov
Keyword(s):  
Least Squares Method ◽  
Main Idea ◽  
Simulated Data ◽  
Real Data ◽  
Model Parameters ◽  
Nonlinear Method ◽  
Ligand Interaction ◽  
Receptor Ligand ◽  
Parameter Values ◽  
True Values

As a rule, receptor-ligand assay data are fitted by logistic functions (4PL model, 5PL model, Feldman’s model). The preparation of the initial estimates for parameters of these functions is an important problem for processing receptor-ligand interaction data. This study represents a new mathematical approach to calculate the initial estimates more closely to the true values of parameters. The main idea of this approach is in using the modified linear least squares method for calculations of the parameters for the 4PL model and the Feldman’s model. In this study, the convergence of model parameters to true values is verified for the simulated data with different statistical scatter. Also, the results of processing real data for the 4PL model and the Feldman’s model are presented. A comparison is made of the parameter values calculated by the presented and a nonlinear method. The developed approach has demonstrated its efficiency in calculating the parameters of the complex Feldman”s models up to 4 ligands and 4 sites.

Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data

2019 ◽  
Vol 36 (06) ◽  
pp. 1940011
Author(s):  
Giulia Pedrielli ◽  
K. Selcuk Candan ◽  
Xilun Chen ◽  
Logan Mathesen ◽  
Alireza Inanalouganji ◽  
...  
Keyword(s):  
Dynamical System ◽  
Decision Making ◽  
Dynamical Systems ◽  
Real Time ◽  
Simulated Data ◽  
Simulation Models ◽  
Real Data ◽  
Learning Framework ◽  
Main Challenge ◽  
Specific Scenario

Real-time decision making has acquired increasing interest as a means to efficiently operating complex systems. The main challenge in achieving real-time decision making is to understand how to develop next generation optimization procedures that can work efficiently using: (i) real data coming from a large complex dynamical system, (ii) simulation models available that reproduce the system dynamics. While this paper focuses on a different problem with respect to the literature in RL, the methods proposed in this paper can be used as a support in a sequential setting as well. The result of this work is the new Generalized Ordinal Learning Framework (GOLF) that utilizes simulated data interpreting them as low accuracy information to be intelligently collected offline and utilized online once the scenario is revealed to the user. GOLF supports real-time decision making on complex dynamical systems once a specific scenario is realized. We show preliminary results of the proposed techniques that motivate the authors in further pursuing the presented ideas.

scholarly journals On Modeling Concrete Compressive Strength Data Using Laplace Birnbaum-Saunders Distribution Assuming Contaminated Information

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 830
Author(s):  
Farouq Mohammad A. Alam ◽  
Mazen Nassar
Keyword(s):  
Compressive Strength ◽  
Optimization Problems ◽  
Probability Model ◽  
Simulated Data ◽  
Real Data ◽  
Reliability Function ◽  
Stress Factors ◽  
Model Parameters ◽  
Data Set ◽  
Suggested Approach

Compressive strength is a well-known measurement to evaluate the endurance of a given concrete mixture to stress factors, such as compressive loads. A suggested approach to assess compressive strength of concrete is to assume that it follows a probability model from which its reliability is calculated. In reliability analysis, a probability distribution’s reliability function is used to calculate the probability of a specimen surviving to a certain threshold without damage. To approximate the reliability of a subject of interest, one must estimate the corresponding parameters of the probability model. Researchers typically formulate an optimization problem, which is often nonlinear, based on the maximum likelihood theory to obtain estimates for the targeted parameters and then estimate the reliability. Nevertheless, there are additional nonlinear optimization problems in practice from which different estimators for the model parameters are obtained once they are solved numerically. Under normal circumstances, these estimators may perform similarly. However, some might become more robust under irregular situations, such as in the case of data contamination. In this paper, nine frequentist estimators are derived for the parameters of the Laplace Birnbaum-Saunders distribution and then applied to a simulated data set and a real data set. Afterwards, they are compared numerically via Monte Carlo comparative simulation study. The resulting estimates for the reliability based on these estimators are also assessed in the latter study.

scholarly journals Bayesian learning ecosystem dynamics with delayed dependencies from incomplete multiple source data : an application to plant epidemiology

2018 ◽  
Author(s):  
Stéphane Dupas
Keyword(s):  
Missing Data ◽  
Bayesian Network ◽  
Bayesian Learning ◽  
Network Inference ◽  
Simulated Data ◽  
Real Data ◽  
Multiple Source ◽  
Ecosystem Dynamics ◽  
Model Parameters ◽  
Simulation Based

AbstractEcosystem dynamics forecasting is central to major problems in ecology, society, and economy. The existing models serve as decision tools but their parameters valitity are usually not confronted to real data in a formalized approach. Dynamics bayesian network inference is promissing but limited when dealing with incomplete multiple source time series with delayed time dependencies. We propose here a temporal bayesian network with time delay and aproximate inference algorithm, to learn altogether cryptic ecosystem variables, missing data, and model parameters. The novelty in the approach is that it combines simulation-based and likelihood-based aproximate bayesian inference. The advantage of simulation based is that it allows to sample hidden processes. The advantage of likelihood based is that it provides a summary statistics that is really representing the model we are interested in. The ecosystem variables and the missing data are simulated from indicator variables using the probabilistic indicator-ecosystem model. The likelihood is estimated by averaging the probability of observed-simulated data over simulations, the parameter space is sampled with Metropolis Hasting algorithm. Another innovative proposition is to parametrize the network structure in order to learn model structure within a space provided by prior distribution. We apply to plant epidemiology.

A maximum likelihood approach for identifying dive bouts improves accuracy, precision and objectivity

Behaviour ◽  
2007 ◽  
Vol 144 (11) ◽  
pp. 1315-1332 ◽  
Author(s):  
Sebastián Luque ◽  
Christophe Guinet
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Real Data ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Systematic Effect ◽  
Fur Seals ◽  
Accuracy And Precision

AbstractForaging behaviour frequently occurs in bouts, and considerable efforts to properly define those bouts have been made because they partly reflect different scales of environmental variation. Methods traditionally used to identify such bouts are diverse, include some level of subjectivity, and their accuracy and precision is rarely compared. Therefore, the applicability of a maximum likelihood estimation method (MLM) for identifying dive bouts was investigated and compared with a recently proposed sequential differences analysis (SDA). Using real data on interdive durations from Antarctic fur seals (Arctocephalus gazella Peters, 1875), the MLM-based model produced briefer bout ending criterion (BEC) and more precise parameter estimates than the SDA approach. The MLM-based model was also in better agreement with real data, as it predicted the cumulative frequency of differences in interdive duration more accurately. Using both methods on simulated data showed that the MLM-based approach produced less biased estimates of the given model parameters than the SDA approach. Different choices of histogram bin widths involved in SDA had a systematic effect on the estimated BEC, such that larger bin widths resulted in longer BECs. These results suggest that using the MLM-based procedure with the sequential differences in interdive durations, and possibly other dive characteristics, may be an accurate, precise, and objective tool for identifying dive bouts.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

scholarly journals Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Aneta Sawikowska ◽  
Anna Piasecka ◽  
Piotr Kachlicki ◽  
Paweł Krajewski
Keyword(s):  
Simulated Data ◽  
Principal Component ◽  
Real Data ◽  
Functional Principal Component Analysis ◽  
Additional Advantage ◽  
Time Alignment ◽  
Peak Separation ◽  
Biological Mixtures ◽  
Overlapping Peaks ◽  
Retention Time Alignment

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

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