scholarly journals Data Mining in Automotive Warranty Analysis

2021 ◽  
Author(s):  
◽  
Her Guan Teo
Keyword(s):  
Data Mining ◽  
Standard Error ◽  
Real Data ◽  
Calendar Time ◽  
Bivariate Case ◽  
Linear Approach ◽  
Univariate Case ◽  
Warranty Cost ◽  
The Mean ◽  
Reporting Delay

<p>This thesis is about data mining in automotive warranty analysis, with an emphasis on modeling the mean cumulative warranty cost or number of claims (per vehicle). In our study, we deal with a type of truncation that is typical for automotive warranty data, where the warranty coverage and the resulting warranty data are limited by age and mileage. Age, as a function of time, is known for all sold vehicles at all time. However, mileage is only observed for a vehicle with at least one claim and only at the time of the claim. To deal with this problem of incomplete mileage information, we consider a linear approach and a piece-wise linear approach within a nonparametric framework. We explore the univariate case, as well as the bivariate case. For the univariate case, we evaluate the mean cumulative warranty cost and its standard error as a function of age, a function of mileage, and a function of actual (calendar) time. For the bivariate case, we evaluate the mean cumulative warranty cost as a function of age and mileage. The effect of reporting delay of claim and several methods for making prediction are also considered. Throughout this thesis, we illustrate the ideas using examples based on real data.</p>

scholarly journals Data Mining in Automotive Warranty Analysis

2021 ◽  
Author(s):  
◽  
Her Guan Teo
Keyword(s):  
Data Mining ◽  
Standard Error ◽  
Real Data ◽  
Calendar Time ◽  
Bivariate Case ◽  
Linear Approach ◽  
Univariate Case ◽  
Warranty Cost ◽  
The Mean ◽  
Reporting Delay

<p>This thesis is about data mining in automotive warranty analysis, with an emphasis on modeling the mean cumulative warranty cost or number of claims (per vehicle). In our study, we deal with a type of truncation that is typical for automotive warranty data, where the warranty coverage and the resulting warranty data are limited by age and mileage. Age, as a function of time, is known for all sold vehicles at all time. However, mileage is only observed for a vehicle with at least one claim and only at the time of the claim. To deal with this problem of incomplete mileage information, we consider a linear approach and a piece-wise linear approach within a nonparametric framework. We explore the univariate case, as well as the bivariate case. For the univariate case, we evaluate the mean cumulative warranty cost and its standard error as a function of age, a function of mileage, and a function of actual (calendar) time. For the bivariate case, we evaluate the mean cumulative warranty cost as a function of age and mileage. The effect of reporting delay of claim and several methods for making prediction are also considered. Throughout this thesis, we illustrate the ideas using examples based on real data.</p>

scholarly journals Video capture of crustacean fisheries data as an alternative to on-board observers

2015 ◽  
Vol 72 (6) ◽  
pp. 1811-1821 ◽  
Author(s):  
Natalie Hold ◽  
Lee G. Murray ◽  
Julia R. Pantin ◽  
Jodie A. Haig ◽  
Hilmar Hinz ◽  
...  
Keyword(s):  
Data Collection ◽  
Standard Error ◽  
Sex Allocation ◽  
Carapace Width ◽  
Real Data ◽  
Mean Difference ◽  
Video Data ◽  
Camera Systems ◽  
Growth Increments ◽  
The Mean

Abstract For EU member states to meet the requirements of the Marine Strategy Framework Directive and the reformed Common Fisheries Policy, it will be necessary to improve data collection related to many fisheries that are at present subject to relatively little monitoring or scientific research. This study evaluated the use of on-board camera systems to collect data from Cancer pagurus and Homarus gammarus fisheries. We evaluated the reliability of the hardware and its ability to collect images of sufficient accuracy and precision compared with using on-board observers. Fishers and on-board observers passed animals removed from traps across a defined area. The relationship between the in situ and predicted measurements of carapace length of lobsters or carapace width (CW) of crabs was investigated. The mean difference between the predicted and real crab measurements was −0.853 mm with a standard error of 0.378 mm. Suggesting that the model tends to underestimate the real CW slightly. The mean difference between predicted and real data for lobsters was 0.085 mm with a standard error of 0.208 mm. Sex allocation for crabs based on video images was 100% accurate. All male lobsters were correctly assigned. For lobsters &gt;86 mm in length, the correct female sex allocation was 100% accurate. For smaller lobsters, the accuracy of sex allocation decreased to a low of 51% in lobsters &lt;70 mm. Camera systems were found to be a suitable method for collecting data on the size and sex of crabs and lobsters. The error attributable to using video data rather than manual measurement was less than 3 mm, which is sufficient to detect growth increments in these species. The requirements to collect basic species data are increasing and the ability to do so without on-board observers will reduce the cost implications of these requirements. Future computer automation of image extraction and measurements will increase the application of video systems for data collection.

Multi-Center Calibration of the Second Reference Material for Thromboplastin, Rabbit, Plain, Coded CRM 149R

1991 ◽  
Vol 65 (03) ◽  
pp. 263-267 ◽  
Author(s):  
A M H P van den Besselaar ◽  
R M Bertina
Keyword(s):  
Reference Material ◽  
Standard Error ◽  
Standard Errors ◽  
Sensitivity Index ◽  
Long Term Stability ◽  
Significant Bias ◽  
The Mean ◽  
The Relationship ◽  
International Sensitivity Index

SummaryIn a collaborative trial of eleven laboratories which was performed mainly within the framework of the European Community Bureau of Reference (BCR), a second reference material for thromboplastin, rabbit, plain, was calibrated against its predecessor RBT/79. This second reference material (coded CRM 149R) has a mean International Sensitivity Index (ISI) of 1.343 with a standard error of the mean of 0.035. The standard error of the ISI was determined by combination of the standard errors of the ISI of RBT/79 and the slope of the calibration line in this trial.The BCR reference material for thromboplastin, human, plain (coded BCT/099) was also included in this trial for assessment of the long-term stability of the relationship with RBT/79. The results indicated that this relationship has not changed over a period of 8 years. The interlaboratory variation of the slope of the relationship between CRM 149R and RBT/79 was significantly lower than the variation of the slope of the relationship between BCT/099 and RBT/79. In addition to the manual technique, a semi-automatic coagulometer according to Schnitger & Gross was used to determine prothrombin times with CRM 149R. The mean ISI of CRM 149R was not affected by replacement of the manual technique by this particular coagulometer.Two lyophilized plasmas were included in this trial. The mean slope of relationship between RBT/79 and CRM 149R based on the two lyophilized plasmas was the same as the corresponding slope based on fresh plasmas. Tlowever, the mean slope of relationship between RBT/79 and BCT/099 based on the two lyophilized plasmas was 4.9% higher than the mean slope based on fresh plasmas. Thus, the use of these lyophilized plasmas induced a small but significant bias in the slope of relationship between these thromboplastins of different species.

scholarly journals Turbulent fluxes of entropy and internal energy in temperature stratified flows

2015 ◽  
Vol 81 (5) ◽  
Author(s):  
I. Rogachevskii ◽  
N. Kleeorin
Keyword(s):  
Mach Number ◽  
Internal Energy ◽  
Turbulent Flux ◽  
Stratified Flows ◽  
Fluid Temperature ◽  
Stratified Turbulence ◽  
Convective Flux ◽  
Low Mach Number ◽  
Linear Approach ◽  
The Mean

We derive equations for the mean entropy and the mean internal energy in low-Mach-number temperature stratified turbulence (i.e. for turbulent convection or stably stratified turbulence), and show that turbulent flux of entropy is given by$\boldsymbol{F}_{s}=\overline{{\it\rho}}\,\overline{\boldsymbol{u}s}$, where$\overline{{\it\rho}}$is the mean fluid density,$s$is fluctuation of entropy and overbars denote averaging over an ensemble of turbulent velocity fields,$\boldsymbol{u}$. We demonstrate that the turbulent flux of entropy is different from the turbulent convective flux,$\boldsymbol{F}_{c}=\overline{T}\,\overline{{\it\rho}}\,\overline{\boldsymbol{u}s}$, of the fluid internal energy, where$\overline{T}$is the mean fluid temperature. This turbulent convective flux is well-known in the astrophysical and geophysical literature, and it cannot be used as a turbulent flux in the equation for the mean entropy. This result is exact for low-Mach-number temperature stratified turbulence and is independent of the model used. We also derive equations for the velocity–entropy correlation,$\overline{\boldsymbol{u}s}$, in the limits of small and large Péclet numbers, using the quasi-linear approach and the spectral${\it\tau}$approximation, respectively. This study is important in view of different applications to astrophysical and geophysical temperature stratified turbulence.

scholarly journals An alternative to the rejection of observations

1932 ◽  
Vol 137 (831) ◽  
pp. 78-87 ◽  
Keyword(s):  
Standard Deviation ◽  
Continuous Function ◽  
Large Deviations ◽  
Opposite Direction ◽  
Standard Error ◽  
The Other ◽  
Cause Of Error ◽  
The Mean ◽  
Normal Law ◽  
Usual Rule

1. It is widely felt that any method of rejecting observations with large deviations from the mean is open to some suspicion. Suppose that by some criterion, such as Peirce’s and Chauvenet’s, we decide to reject observations with deviations greater than 4 σ, where σ is the standard error, computed from the standard deviation by the usual rule; then we reject an observation deviating by 4·5 σ, and thereby alter the mean by about 4·5 σ/ n , where n is the number of observations, and at the same time we reduce the computed standard error. This may lead to the rejection of another observation deviating from the original mean by less than 4 σ, and if the process is repeated the mean may be shifted so much as to lead to doubt as to whether it is really sufficiently representative of the observations. In many cases, where we suspect that some abnormal cause has affected a fraction of the observations, there is a legitimate doubt as to whether it has affected a particular observation. Suppose that we have 50 observations. Then there is an even chance, according to the normal law, of a deviation exceeding 2·33 σ. But a deviation of 3 σ or more is not impossible, and if we make a mistake in rejecting it the mean of the remainder is not the most probable value. On the other hand, an observation deviating by only 2 σ may be affected by an abnormal cause of error, and then we should err in retaining it, even though no existing rule will instruct us to reject such an observation. It seems clear that the probability that a given observation has been affected by an abnormal cause of error is a continuous function of the deviation; it is never certain or impossible that it has been so affected, and a process that completely rejects certain observations, while retaining with full weight others with comparable deviations, possibly in the opposite direction, is unsatisfactory in principle.

scholarly journals Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

2013 ◽  
Vol 19 (3) ◽  
pp. 177 ◽  
Author(s):  
Lily Tapak ◽  
Hossein Mahjub ◽  
Omid Hamidi ◽  
Jalal Poorolajal
Keyword(s):  
Data Mining ◽  
Real Data ◽  
Data Comparison ◽  
Mining Methods ◽  
Prediction Of Diabetes

Velocity inversion by global optimization using finite-offset common-reflection-surface stacking applied to synthetic and Tacutu Basin seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R165-R174 ◽  
Author(s):  
Marcelo Jorge Luz Mesquita ◽  
João Carlos Ribeiro Cruz ◽  
German Garabito Callapino
Keyword(s):  
Objective Function ◽  
Real Data ◽  
Velocity Model ◽  
Data Sets ◽  
Layer By Layer ◽  
Velocity Inversion ◽  
Very Fast Simulated Annealing ◽  
Target Layer ◽  
Common Reflection Surface ◽  
The Mean

Estimation of an accurate velocity macromodel is an important step in seismic imaging. We have developed an approach based on coherence measurements and finite-offset (FO) beam stacking. The algorithm is an FO common-reflection-surface tomography, which aims to determine the best layered depth-velocity model by finding the model that maximizes a semblance objective function calculated from the amplitudes in common-midpoint (CMP) gathers stacked over a predetermined aperture. We develop the subsurface velocity model with a stack of layers separated by smooth interfaces. The algorithm is applied layer by layer from the top downward in four steps per layer. First, by automatic or manual picking, we estimate the reflection times of events that describe the interfaces in a time-migrated section. Second, we convert these times to depth using the velocity model via application of Dix’s formula and the image rays to the events. Third, by using ray tracing, we calculate kinematic parameters along the central ray and build a paraxial FO traveltime approximation for the FO common-reflection-surface method. Finally, starting from CMP gathers, we calculate the semblance of the selected events using this paraxial traveltime approximation. After repeating this algorithm for all selected CMP gathers, we use the mean semblance values as an objective function for the target layer. When this coherence measure is maximized, the model is accepted and the process is completed. Otherwise, the process restarts from step two with the updated velocity model. Because the inverse problem we are solving is nonlinear, we use very fast simulated annealing to search the velocity parameters in the target layers. We test the method on synthetic and real data sets to study its use and advantages.

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