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SURVIVAL SURVIVAL Students
SEQUENCE SURVIVORS: ANALYSIS OF GRADUATION OR TRANSFER IN THREE YEARS OR LESS By: Leila Jamoosian Terrence Willett CAIR 2018 Anaheim WHY DO WE WANT STUDENTS TO NOT “SURVIVE” COLLEGE MORE QUICKLY Traditional survival analysis tracks individuals until they perish Applied to a college setting, perishing is completing a degree or transfer Past analyses have given many years to allow students to show a completion (e.g. 8 years) With new incentives, completion in 3 years is critical WHY SURVIVAL NOT LINEAR REGRESSION? Time is not normally distributed No approach to deal with Censors MAIN GOAL OF STUDY To estimate time that students graduate or transfer in three years or less(time-to-event=1/ incidence rate) for a group of individuals We can compare time to graduate or transfer in three years between two of more groups Or assess the relation of co-variables to the three years graduation event such as number of credit, full time status, number of withdraws, placement type, successfully pass at least transfer level Math or English courses,… Definitions: Time-to-event: Number of months from beginning the program until graduating Outcome: Completing AA/AS degree in three years or less Censoring: Students who drop or those who do not graduate in three years or less (takes longer to graduate or fail) Note 1: Students who completed other goals such as certificate of achievement or skill certificate were not counted as success in this analysis Note 2: First timers who got their first degree at Cabrillo (they may come back for second degree -
Survival Analysis: an Exact Method for Rare Events
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 12-2020 Survival Analysis: An Exact Method for Rare Events Kristina Reutzel Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/gradreports Part of the Survival Analysis Commons Recommended Citation Reutzel, Kristina, "Survival Analysis: An Exact Method for Rare Events" (2020). All Graduate Plan B and other Reports. 1509. https://digitalcommons.usu.edu/gradreports/1509 This Creative Project is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Plan B and other Reports by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. SURVIVAL ANALYSIS: AN EXACT METHOD FOR RARE EVENTS by Kristi Reutzel A project submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in STATISTICS Approved: Christopher Corcoran, PhD Yan Sun, PhD Major Professor Committee Member Daniel Coster, PhD Committee Member Abstract Conventional asymptotic methods for survival analysis work well when sample sizes are at least moderately sufficient. When dealing with small sample sizes or rare events, the results from these methods have the potential to be inaccurate or misleading. To handle such data, an exact method is proposed and compared against two other methods: 1) the Cox proportional hazards model and 2) stratified logistic regression for discrete survival analysis data. Background and Motivation Survival analysis models are used for data in which the interest is time until a specified event. The event of interest could be heart attack, onset of disease, failure of a mechanical system, cancellation of a subscription service, employee termination, etc. -
Research Participants' Rights to Data Protection in the Era of Open Science
DePaul Law Review Volume 69 Issue 2 Winter 2019 Article 16 Research Participants’ Rights To Data Protection In The Era Of Open Science Tara Sklar Mabel Crescioni Follow this and additional works at: https://via.library.depaul.edu/law-review Part of the Law Commons Recommended Citation Tara Sklar & Mabel Crescioni, Research Participants’ Rights To Data Protection In The Era Of Open Science, 69 DePaul L. Rev. (2020) Available at: https://via.library.depaul.edu/law-review/vol69/iss2/16 This Article is brought to you for free and open access by the College of Law at Via Sapientiae. It has been accepted for inclusion in DePaul Law Review by an authorized editor of Via Sapientiae. For more information, please contact [email protected]. \\jciprod01\productn\D\DPL\69-2\DPL214.txt unknown Seq: 1 21-APR-20 12:15 RESEARCH PARTICIPANTS’ RIGHTS TO DATA PROTECTION IN THE ERA OF OPEN SCIENCE Tara Sklar and Mabel Crescioni* CONTENTS INTRODUCTION ................................................. 700 R I. REAL-WORLD EVIDENCE AND WEARABLES IN CLINICAL TRIALS ....................................... 703 R II. DATA PROTECTION RIGHTS ............................. 708 R A. General Data Protection Regulation ................. 709 R B. California Consumer Privacy Act ................... 710 R C. GDPR and CCPA Influence on State Data Privacy Laws ................................................ 712 R III. RESEARCH EXEMPTION AND RELATED SAFEGUARDS ... 714 R IV. RESEARCH PARTICIPANTS’ DATA AND OPEN SCIENCE . 716 R CONCLUSION ................................................... 718 R Clinical trials are increasingly using sensors embedded in wearable de- vices due to their capabilities to generate real-world data. These devices are able to continuously monitor, record, and store physiological met- rics in response to a given therapy, which is contributing to a redesign of clinical trials around the world. -
Overview of Reliability Engineering
Overview of reliability engineering Eric Marsden <[email protected]> Context ▷ I have a fleet of airline engines and want to anticipate when they mayfail ▷ I am purchasing pumps for my refinery and want to understand the MTBF, lambda etc. provided by the manufacturers ▷ I want to compare different system designs to determine the impact of architecture on availability 2 / 32 Reliability engineering ▷ Reliability engineering is the discipline of ensuring that a system will function as required over a specified time period when operated and maintained in a specified manner. ▷ Reliability engineers address 3 basic questions: • When does something fail? • Why does it fail? • How can the likelihood of failure be reduced? 3 / 32 The termination of the ability of an item to perform a required function. [IEV Failure Failure 191-04-01] ▷ A failure is always related to a required function. The function is often specified together with a performance requirement (eg. “must handleup to 3 tonnes per minute”, “must respond within 0.1 seconds”). ▷ A failure occurs when the function cannot be performed or has a performance that falls outside the performance requirement. 4 / 32 The state of an item characterized by inability to perform a required function Fault Fault [IEV 191-05-01] ▷ While a failure is an event that occurs at a specific point in time, a fault is a state that will last for a shorter or longer period. ▷ When a failure occurs, the item enters the failed state. A failure may occur: • while running • while in standby • due to demand 5 / 32 Discrepancy between a computed, observed, or measured value or condition and Error Error the true, specified, or theoretically correct value or condition. -
Lochner</I> in Cyberspace: the New Economic Orthodoxy Of
Georgetown University Law Center Scholarship @ GEORGETOWN LAW 1998 Lochner in Cyberspace: The New Economic Orthodoxy of "Rights Management" Julie E. Cohen Georgetown University Law Center, [email protected] This paper can be downloaded free of charge from: https://scholarship.law.georgetown.edu/facpub/811 97 Mich. L. Rev. 462-563 (1998) This open-access article is brought to you by the Georgetown Law Library. Posted with permission of the author. Follow this and additional works at: https://scholarship.law.georgetown.edu/facpub Part of the Intellectual Property Law Commons, Internet Law Commons, and the Law and Economics Commons LOCHNER IN CYBERSPACE: THE NEW ECONOMIC ORTHODOXY OF "RIGHTS MANAGEMENT" JULIE E. COHEN * Originally published 97 Mich. L. Rev. 462 (1998). TABLE OF CONTENTS I. THE CONVERGENCE OF ECONOMIC IMPERATIVES AND NATURAL RIGHTS..........6 II. THE NEW CONCEPTUALISM.....................................................18 A. Constructing Consent ......................................................18 B. Manufacturing Scarcity.....................................................32 1. Transaction Costs and Common Resources................................33 2. Incentives and Redistribution..........................................40 III. ON MODELING INFORMATION MARKETS........................................50 A. Bargaining Power and Choice in Information Markets.............................52 1. Contested Exchange and the Power to Switch.............................52 2. Collective Action, "Rent-Seeking," and Public Choice.......................67 -
Understanding Functional Safety FIT Base Failure Rate Estimates Per IEC 62380 and SN 29500
www.ti.com Technical White Paper Understanding Functional Safety FIT Base Failure Rate Estimates per IEC 62380 and SN 29500 Bharat Rajaram, Senior Member, Technical Staff, and Director, Functional Safety, C2000™ Microcontrollers, Texas Instruments ABSTRACT Functional safety standards like International Electrotechnical Commission (IEC) 615081 and International Organization for Standardization (ISO) 262622 require that semiconductor device manufacturers address both systematic and random hardware failures. Systematic failures are managed and mitigated by following rigorous development processes. Random hardware failures must adhere to specified quantitative metrics to meet hardware safety integrity levels (SILs) or automotive SILs (ASILs). Consequently, systematic failures are excluded from the calculation of random hardware failure metrics. Table of Contents 1 Introduction.............................................................................................................................................................................2 2 Types of Faults and Quantitative Random Hardware Failure Metrics .............................................................................. 2 3 Random Failures Over a Product Lifetime and Estimation of BFR ...................................................................................3 4 BFR Estimation Techniques ................................................................................................................................................. 4 5 Siemens SN 29500 FIT model -
Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications
S S symmetry Article Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications Perla Celis 1,*, Rolando de la Cruz 1 , Claudio Fuentes 2 and Héctor W. Gómez 3 1 Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7941169, Chile; [email protected] 2 Department of Statistics, Oregon State University, 217 Weniger Hall, Corvallis, OR 97331, USA; [email protected] 3 Departamento de Matemática, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile; [email protected] * Correspondence: [email protected] Abstract: We introduce a new class of distributions called the epsilon–positive family, which can be viewed as generalization of the distributions with positive support. The construction of the epsilon– positive family is motivated by the ideas behind the generation of skew distributions using symmetric kernels. This new class of distributions has as special cases the exponential, Weibull, log–normal, log–logistic and gamma distributions, and it provides an alternative for analyzing reliability and survival data. An interesting feature of the epsilon–positive family is that it can viewed as a finite scale mixture of positive distributions, facilitating the derivation and implementation of EM–type algorithms to obtain maximum likelihood estimates (MLE) with (un)censored data. We illustrate the flexibility of this family to analyze censored and uncensored data using two real examples. One of them was previously discussed in the literature; the second one consists of a new application to model recidivism data of a group of inmates released from the Chilean prisons during 2007. -
A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess
S S symmetry Article A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess Guillermo Martínez-Flórez 1, Víctor Leiva 2,* , Emilio Gómez-Déniz 3 and Carolina Marchant 4 1 Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad de Córdoba, Montería 14014, Colombia; [email protected] 2 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile 3 Facultad de Economía, Empresa y Turismo, Universidad de Las Palmas de Gran Canaria and TIDES Institute, 35001 Canarias, Spain; [email protected] 4 Facultad de Ciencias Básicas, Universidad Católica del Maule, 3466706 Talca, Chile; [email protected] * Correspondence: [email protected] or [email protected] Received: 30 June 2020; Accepted: 19 August 2020; Published: 1 September 2020 Abstract: In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal. Keywords: beta distribution; centered skew-normal distribution; maximum-likelihood methods; Monte Carlo simulations; proportions; R software; rates; zero/one inflated data 1. -
Probability Analysis in Determining the Behaviour of Variable Amplitude Strain Signal Based on Extraction Segments
2141 Probability Analysis in Determining the Behaviour of Variable Amplitude Strain Signal Based on Extraction Segments Abstract M. F. M. Yunoh a This paper focuses on analysis in determining the behaviour of vari- S. Abdullah b, * able amplitude strain signals based on extraction of segments. The M. H. M. Saad c constant Amplitude loading (CAL), that was used in the laboratory d Z. M. Nopiah tests, was designed according to the variable amplitude loading e (VAL) from Society of Automotive Engineers (SAE). The SAE strain M. Z. Nuawi signal was then edited to obtain those segments that cause fatigue a damage to components. The segments were then sorted according to Department of Mechanical and Materials their amplitude and were used as a reference in the design of the Engineering, Faculty of Engineering & CAL loading for the laboratory tests. The strain signals that were Built Enviroment, Universiti Kebangsaan obtained from the laboratory tests were then analysed using fatigue Malaysia, 43600 UKM Bangi, Selangor, life prediction approach and statistics, i.e. Weibull distribution anal- Malaysia. [email protected] b ysis. Based on the plots of the Probability Density Function (PDF), [email protected] c Cumulative Distribution Function (CDF) and the probability of fail- [email protected]; d ure in the Weibull distribution analysis, it was shown that more than [email protected]; e 70% failure occurred when the number of cycles approached 1.0 x [email protected] 1011. Therefore, the Weibull distribution analysis can be used as an alternative to predict the failure probability. * Corresponding author: S. -
Survival Analysis on Duration Data in Intelligent Tutors
Survival Analysis on Duration Data in Intelligent Tutors Michael Eagle and Tiffany Barnes North Carolina State University, Department of Computer Science, 890 Oval Drive, Campus Box 8206 Raleigh, NC 27695-8206 {mjeagle,tmbarnes}@ncsu.edu Abstract. Effects such as student dropout and the non-normal distribu- tion of duration data confound the exploration of tutor efficiency, time-in- tutor vs. tutor performance, in intelligent tutors. We use an accelerated failure time (AFT) model to analyze the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. AFT is a branch of survival analysis, a statistical technique designed for measur- ing time-to-event data and account for participant attrition. We found that students provided with automatically generated hints were able to complete the tutor in about half the time taken by students who were not provided hints. We compare the results of survival analysis with a standard between-groups mean comparison and show how failing to take student dropout into account could lead to incorrect conclusions. We demonstrate that survival analysis is applicable to duration data col- lected from intelligent tutors and is particularly useful when a study experiences participant attrition. Keywords: ITS, EDM, Survival Analysis, Efficiency, Duration Data 1 Introduction Intelligent tutoring systems have sizable effects on student learning efficiency | spending less time to achieve equal or better performance. In a classic example, students who used the LISP tutor spent 30% less time and performed 43% better on posttests when compared to a self-study condition [2]. While this result is quite famous, few papers have focused on differences between tutor interventions in terms of the total time needed by students to complete the tutor. -
Establishing the Discrete-Time Survival Analysis Model
What will we cover? §11.1 p.358 Specifying a suitable DTSA model §11.2 p.369 Establishing the Discrete-Time Fitting the DTSA model to data §11.3 p.378 Survival Analysis Model Interpreting the parameter estimates §11.4 p.386 (ALDA, Ch. 11) Displaying fitted hazard and survivor §11.5 p.391 functions Comparing DTSA models using §11.6 p.397 goodness-of-fit statistics. John Willett & Judy Singer Harvard University Graduate School of Education May, 2003 1 Specifying the DTSA Model Specifying the DTSA Model Data Example: Grade at First Intercourse Extract from the Person-Level & Person-Period Datasets Grade at First Intercourse (ALDA, Fig. 11.5, p. 380) • Research Question: Whether, and when, Not censored ⇒ did adolescent males experience heterosexual experience the event intercourse for the first time? Censored ⇒ did not • Citation: Capaldi, et al. (1996). experience the event • Sample: 180 high-school boys. • Research Design: – Event of interest is the first experience of heterosexual intercourse. Boy #193 was – Boys tracked over time, from 7th thru 12th grade. tracked until – 54 (30% of sample) were virgins (censored) at end he had sex in 9th grade. of data collection. Boy #126 was tracked until he had sex in 12th grade. Boy #407 was censored, remaining a virgin until he graduated. 2 Specifying the DTSA Model Specifying the DTSA Model Estimating Sample Hazard & Survival Probabilities Sample Hazard & Survivor Functions Grade at First Intercourse (ALDA, Table 11.1, p. 360) Grade at First Intercourse (ALDA, Fig. 10.2B, p. 340) Discrete-Time Hazard is the Survival probability describes the conditional probability that the chance that a person will survive event will occur in the period, beyond the period in question given that it hasn’t occurred without experiencing the event: earlier: S(t j ) = Pr{T > j} h(t j ) = Pr{T = j | T > j} Estimated by cumulating hazard: Estimated by the corresponding ˆ ˆ ˆ sample probability: S(t j ) = S(t j−1)[1− h(t j )] n events ˆ j e.g., h(t j ) = n at risk j ˆ ˆ ˆ S(t9 ) = S(t8 )[1− h(t9 )] e.g. -
Log-Epsilon-S Ew Normal Distribution and Applications
Log-Epsilon-Skew Normal Distribution and Applications Terry L. Mashtare Jr. Department of Biostatistics University at Bu®alo, 249 Farber Hall, 3435 Main Street, Bu®alo, NY 14214-3000, U.S.A. Alan D. Hutson Department of Biostatistics University at Bu®alo, 249 Farber Hall, 3435 Main Street, Bu®alo, NY 14214-3000, U.S.A. Roswell Park Cancer Institute, Elm & Carlton Streets, Bu®alo, NY 14263, U.S.A. Govind S. Mudholkar Department of Statistics University of Rochester, Rochester, NY 14627, U.S.A. August 20, 2009 Abstract In this note we introduce a new family of distributions called the log- epsilon-skew-normal (LESN) distribution. This family of distributions can trace its roots back to the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000). A key advantage of our model is that the 1 well known lognormal distribution is a subclass of the LESN distribution. We study its main properties, hazard function, moments, skewness and kurtosis coe±cients, and discuss maximum likelihood estimation of model parameters. We summarize the results of a simulation study to examine the behavior of the maximum likelihood estimates, and we illustrate the maximum likelihood estimation of the LESN distribution parameters to a real world data set. Keywords: Lognormal distribution, maximum likelihood estimation. 1 Introduction In this note we introduce a new family of distributions called the log-epsilon-skew- normal (LESN) distribution. This family of distributions can trace its roots back to the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000) [14]. A key advantage of our model is that the well known lognormal distribu- tion is a subclass of the LESN distribution.