Optimization Performance of a CO[subscript 2] Pulsed Tuneable Laser

Science.gov (United States)

Ribeiro, J. H. F.; Lobo, R. F. M.

2009-01-01

In this paper, a procedure is presented that will allow (i) the power and (ii) the energy of a pulsed and tuneable TEA CO[subscript 2] laser to be optimized. This type of laser represents a significant improvement in performance and portability. Combining a pulse mode with a grating tuning facility, it enables us to scan the working wavelength…

Subscriptions

African Journals Online (AJOL)

Subscriptions Contact. Dr Basil C Ezeanolue Department of Otorhinolaryngology College of Medicine University of Nigeria Teaching Hospital Enugu 400 001. Nigeria Email: editornjorl@yahoo.com. Individuals Nigeria - 600.00 Naira Africa - US$15.00. Other Countries - US$25.00. Institutions Nigeria - 1200.00 Naira

47 CFR 73.641 - Subscription TV definitions.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Subscription TV definitions. 73.641 Section 73.641 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES RADIO BROADCAST SERVICES Television Broadcast Stations § 73.641 Subscription TV definitions. (a) Subscription...

Subscriptions

African Journals Online (AJOL)

Subscriptions Contact. Professor Dele Braimoh P.O. Box 392. UNISA 0003. Pretoria South Africa Email: dbraimoh@yahoo.com. The AJCPSF requires a token payment of N5000 (excluding Review fee) from Nigerian and $150 ( International) contributors for maintaining the initial cost of editorial works on submitted articles ...

subscription information

Indian Academy of Sciences (India)

Administrator

PROCEEDINGS IN ENGINEERING JOURNAL OF. SCIENCE EDUCATION SADHANA (PROCEEDINGS. ENGINEERING SCIENCES) SUBSCRIPTION JOUR-. NAL OF ASTROPHYSICS AND ASTRONOMY. JOURNAL OF BIOSCIENCES JOURNAL OF. CHEMICAL SCIENCES JOURNAL OF EARTH SYS. No. Journal.

Subscriptions

African Journals Online (AJOL)

Subscriptions Contact. Rachel Rege Kenya Agricultural Research Institute, P O Box 57811, Nairobi, KENYA Email: rrege@kari.org. East Africa Kssh 4000; Sterling 40; USA $62; including postage by surface mail. Postage by air mail can be arranged at cost on request. Payments to be made to the Editor, East African ...

Improved Syntheses and Expanded Analyses of the Enantiomerically Enriched Chiral Cobalt Complexes Co(en)[subscript 3]I[subscript 3] and Co(diNOsar)Br[subscript 3

Science.gov (United States)

McClellan, Michael J.; Cass, Marion E.

2015-01-01

This communication is a collection of additions and modifications to two previously published classic inorganic synthesis laboratory experiments. The experimental protocol for the synthesis and isolation of enantiomerically enriched ?- (or ?-)Co(en)[subscript 3]I[subscript 3] has been modified to increase reproducibility, yield, and enantiomeric…

Utilization of office computer for journal subscription

International Nuclear Information System (INIS)

Yonezawa, Minoru; Shimizu, Tokiyo

1993-01-01

Integrated library automation system has been operating in Japan Atomic Energy Research Institute library, which consists of three subsystems for serials control, book acquisition and circulation control. Functions of the serials control subsystem have been improved to reduce the work load of journal subscription work. Subscription price(both in foreign currency and Japanese yen), conversion factor, foreign currency exchange rate are newly introduced as data elements to a master file for automatic calculation and totalization in the system, e.g. conversion of subscription price from foreign currency into Japanese yen. Some kinds of journal lists are also printed out, such as journal subscription list, journal distribution list for each laboratory, etc. (author)

Integral Representation of the Pictorial Proof of Sum of [superscript n][subscript k=1]k[superscript 2] = 1/6n(n+1)(2n+1)

Science.gov (United States)

Kobayashi, Yukio

2011-01-01

The pictorial proof of the sum of [superscript n][subscript k=1] k[superscript 2] = 1/6n(n+1)(2n+1) is represented in the form of an integral. The integral representations are also applicable to the sum of [superscript n][subscript k-1] k[superscript m] (m greater than or equal to 3). These representations reveal that the sum of [superscript…

Using a Subscription Agent for E-Journal Management

Science.gov (United States)

Grogg, Jill E.

2010-01-01

Subscription agents have had to reinvent themselves over the past 15 years as the numbers of print subscriptions have dramatically dwindled. Many libraries have chosen to bypass the subscription agent and its extra fees in favor of dealing directly with the publisher for e-journal and e-journal package procurement and management. Especially in…

Managing Distributed Systems with Smart Subscriptions

Science.gov (United States)

Filman, Robert E.; Lee, Diana D.; Swanson, Keith (Technical Monitor)

2000-01-01

We describe an event-based, publish-and-subscribe mechanism based on using 'smart subscriptions' to recognize weakly-structured events. We present a hierarchy of subscription languages (propositional, predicate, temporal and agent) and algorithms for efficiently recognizing event matches. This mechanism has been applied to the management of distributed applications.

Downregulation of GABA[Subscript A] Receptor Protein Subunits a6, ß2, d, e, ?2, ?, and ?2 in Superior Frontal Cortex of Subjects with Autism

Science.gov (United States)

Fatemi, S. Hossein; Reutiman, Teri J.; Folsom, Timothy D.; Rustan, Oyvind G.; Rooney, Robert J.; Thuras, Paul D.

2014-01-01

We measured protein and mRNA levels for nine gamma-aminobutyric acid A (GABA[subscript A]) receptor subunits in three brain regions (cerebellum, superior frontal cortex, and parietal cortex) in subjects with autism versus matched controls. We observed changes in mRNA for a number of GABA[subscript A] and GABA[subscript B] subunits and overall…

Factors Affecting Energy Barriers for Pyramidal Inversion in Amines and Phosphines: A Computational Chemistry Lab Exercise

Science.gov (United States)

Montgomery, Craig D.

2013-01-01

An undergraduate exercise in computational chemistry that investigates the energy barrier for pyramidal inversion of amines and phosphines is presented. Semiempirical calculations (PM3) of the ground-state and transition-state energies for NR[superscript 1]R[superscript 2]R[superscript 3] and PR[superscript 1]R[superscript 2]R[superscript 3] allow…

Using a Spreadsheet to Solve the Schro¨dinger Equations for the Energies of the Ground Electronic State and the Two Lowest Excited States of H[subscript2

Science.gov (United States)

Ge, Yingbin; Rittenhouse, Robert C.; Buchanan, Jacob C.; Livingston, Benjamin

2014-01-01

We have designed an exercise suitable for a lab or project in an undergraduate physical chemistry course that creates a Microsoft Excel spreadsheet to calculate the energy of the S[subscript 0] ground electronic state and the S[subscript 1] and T[subscript 1] excited states of H[subscript 2]. The spreadsheet calculations circumvent the…

Synthesis and Characterization of a Perovskite Barium Zirconate (BaZrO[subscript 3]): An Experiment for an Advanced Inorganic Chemistry Laboratory

Science.gov (United States)

Thananatthanachon, Todsapon

2016-01-01

In this experiment, the students explore the synthesis of a crystalline solid-state material, barium zirconate (BaZrO3) by two different synthetic methods: (a) the wet chemical method using BaCl[subscript 2]·2H[subscript 2]O and ZrOCl[subscript 2]·8H[subscript 2]O as the precursors, and (b) the solid-state reaction from BaCO[subscript 3] and…

11 CFR 100.111 - Gift, subscription, loan, advance or deposit of money.

Science.gov (United States)

2010-01-01

... 11 Federal Elections 1 2010-01-01 2010-01-01 false Gift, subscription, loan, advance or deposit of... DEFINITIONS (2 U.S.C. 431) Definition of Expenditure (2 U.S.C. 431(9)) § 100.111 Gift, subscription, loan, advance or deposit of money. (a) A purchase, payment, distribution, loan (except for a loan made in...

Non-Exercise Estimation of VO[subscript 2]max Using the International Physical Activity Questionnaire

Science.gov (United States)

Schembre, Susan M.; Riebe, Deborah A.

2011-01-01

Non-exercise equations developed from self-reported physical activity can estimate maximal oxygen uptake (VO[subscript 2]max) as well as sub-maximal exercise testing. The International Physical Activity Questionnaire is the most widely used and validated self-report measure of physical activity. This study aimed to develop and test a VO[subscript…

OAS :: Email subscriptions

Science.gov (United States)

subscriptions Videos Photos Live Webcast Social Media Facebook @oasofficial Facebook Twitter @oas_official Webcast Videos Audios Photos Social Media Facebook Twitter Newsletters Press and Communications Department Rights Actions against Corruption C Children Civil Registry Civil Society Contact Us Culture Cyber

Internet Access from CERN GSM subscriptions

CERN Multimedia

IT Department

2008-01-01

The data service on GSM subscriptions has been improved, allowing CERN users to access the Internet directly. A CERN GSM subscription with data option now allows you to connect to the Internet from a mobile phone or a PC equipped with a GSM modem. The previous access (CERN intranet) still exists. To get access to the new service, you will find all the information on configurations at: http://cern.ch/gprs The use of this service on the Sunrise network is charged on a flat-rate basis (no extra charge related to the volume of downloaded data). Depending on your CERN subscription type (standard or master), you can also connect to foreign GSM data networks (roaming), but this is strongly discouraged, except where absolutely necessary, due to international roaming charges. Telecom Section, IT/CS

K[subscript a] and K[subscript b] from pH and Conductivity Measurements: A General Chemistry Laboratory Exercise

Science.gov (United States)

Nyasulu, Frazier; Moehring, Michael; Arthasery, Phyllis; Barlag, Rebecca

2011-01-01

The acid ionization constant, K[subscript a], of acetic acid and the base ionization constant, K[subscript b], of ammonia are determined easily and rapidly using a datalogger, a pH sensor, and a conductivity sensor. To decrease sample preparation time and to minimize waste, sequential aliquots of a concentrated standard are added to a known volume…

Power Subscription Strategy: Administrator`s Record of Decision.

Energy Technology Data Exchange (ETDEWEB)

United States. Bonneville Power Administration

1998-12-01

The Bonneville Power Administration (BPA) has decided to adopt a Power Subscription Strategy for entering into new power sales contracts with its Pacific Northwest customers. The Strategy equitably distributes the electric power generated by the Federal Columbia River Power System (FCRPS) within the framework of existing law. The Power Subscription Strategy addresses the availability of power; describes power products; lays out strategies for pricing, including risk management; and discusses contract elements. In proceeding with this Subscription Strategy, BPA is guided by and committed to the Fish and Wildlife funding Principles for the BPA announced by the Vice President of the US in September 1998. This Record of Decision (ROD) addresses the issues raised by commenters who responded to BPA`s Power Subscription Strategy Proposal during and after the comment period that began with the release of the Proposal on September 18, 1998. The ROD is organized in approximately the same way as the Proposal and the Power Subscription Strategy that BPA developed based on the comments received. Abbreviations of party names used in citations appear in the section just preceding this introduction; a list of all the commenters follows the text of the ROD.

46 CFR 201.42 - Subscription, authentication of documents.

Science.gov (United States)

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Subscription, authentication of documents. 201.42 Section 201.42 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION POLICY, PRACTICE AND... Subscription, authentication of documents. (a) Documents filed shall be subscribed: (1) By the person or...

The L-Type Voltage-Gated Calcium Channel Ca [subscript V] 1.2 Mediates Fear Extinction and Modulates Synaptic Tone in the Lateral Amygdala

Science.gov (United States)

Temme, Stephanie J.; Murphy, Geoffrey G.

2017-01-01

L-type voltage-gated calcium channels (LVGCCs) have been implicated in both the formation and the reduction of fear through Pavlovian fear conditioning and extinction. Despite the implication of LVGCCs in fear learning and extinction, studies of the individual LVGCC subtypes, Ca[subscript V]1.2 and Ca[subscript V] 1.3, using transgenic mice have…

Nanosized TiO[subscript 2] for Photocatalytic Water Splitting Studied by Oxygen Sensor and Data Logger

Science.gov (United States)

Zhang, Ruinan; Liu, Song; Yuan, Hongyan; Xiao, Dan; Choi, Martin M. F.

2012-01-01

Photocatalytic water splitting by semiconductor photocatalysts has attracted considerable attention in the past few decades. In this experiment, nanosized titanium dioxide (nano-TiO[subscript 2]) particles are used to photocatalytically split water, which is then monitored by an oxygen sensor. Sacrificial reagents such as organics (EDTA) and metal…

Open access versus subscription journals: a comparison of scientific impact.

Science.gov (United States)

Björk, Bo-Christer; Solomon, David

2012-07-17

In the past few years there has been an ongoing debate as to whether the proliferation of open access (OA) publishing would damage the peer review system and put the quality of scientific journal publishing at risk. Our aim was to inform this debate by comparing the scientific impact of OA journals with subscription journals, controlling for journal age, the country of the publisher, discipline and (for OA publishers) their business model. The 2-year impact factors (the average number of citations to the articles in a journal) were used as a proxy for scientific impact. The Directory of Open Access Journals (DOAJ) was used to identify OA journals as well as their business model. Journal age and discipline were obtained from the Ulrich's periodicals directory. Comparisons were performed on the journal level as well as on the article level where the results were weighted by the number of articles published in a journal. A total of 610 OA journals were compared with 7,609 subscription journals using Web of Science citation data while an overlapping set of 1,327 OA journals were compared with 11,124 subscription journals using Scopus data. Overall, average citation rates, both unweighted and weighted for the number of articles per journal, were about 30% higher for subscription journals. However, after controlling for discipline (medicine and health versus other), age of the journal (three time periods) and the location of the publisher (four largest publishing countries versus other countries) the differences largely disappeared in most subcategories except for journals that had been launched prior to 1996. OA journals that fund publishing with article processing charges (APCs) are on average cited more than other OA journals. In medicine and health, OA journals founded in the last 10 years are receiving about as many citations as subscription journals launched during the same period. Our results indicate that OA journals indexed in Web of Science and/or Scopus are

41 CFR 101-25.108 - Multiyear subscriptions for publications.

Science.gov (United States)

2010-07-01

... for publications. 101-25.108 Section 101-25.108 Public Contracts and Property Management Federal...-GENERAL 25.1-General Policies § 101-25.108 Multiyear subscriptions for publications. Subscriptions for periodicals, newspapers, and other publications for which it is known in advance that a continuing requirement...

Managing without a subscription agent: the experience of doing it yourself

Directory of Open Access Journals (Sweden)

Lisa Lovén

2017-11-01

Full Text Available In October 2015 Stockholm University Library (SUB decided to no longer use the services of a subscription agent for managing individual journal subscriptions. Instead, SUB has taken a do-it-yourself (DIY approach to subscriptions management and now renews and orders new journals directly from each publisher. In the light of two years of experience, this article discusses the key findings of this new way of working with subscriptions, the differences between the first and second year of renewing directly with publishers and the pros and cons of not using an agent. The article ends with a few recommendations and things for other libraries to consider before making the decision to do without a subscription agent and explains why SUB has decided to continue with the DIY approach. 'Based on a breakout session presented at the 40th UKSG Annual Conference, Harrogate, April 2017 '

Economics of Scholarly Publishing: Exploring the Causes of Subscription Price Variations of Scholarly Journals in Business Subject-Specific Areas

Science.gov (United States)

Liu, Lewis G.

2011-01-01

This empirical research investigates subscription price variations of scholarly journals in five business subject-specific areas using the semilogarithmic regression model. It has two main purposes. The first is to address the unsettled debate over whether or not and to what extent commercial publishers reap monopoly profits by overcharging…

Pessimism, Trauma, Risky Sex: Covariates of Depression in College Students

Science.gov (United States)

Swanholm, Eric; Vosvick, Mark; Chng, Chwee-Lye

2009-01-01

Objective: To explain variance in depression in students (N = 648) using a model incorporating sexual trauma, pessimism, and risky sex. Method: Survey data collected from undergraduate students receiving credit for participation. Results: Controlling for demographics, a hierarchical linear regression analysis [Adjusted R[superscript 2] = 0.34,…

Food advertising on children's popular subscription television channels in Australia.

Science.gov (United States)

Hebden, Lana; King, Lesley; Chau, Josephine; Kelly, Bridget

2011-04-01

Trends on Australian free-to-air television show children continue to be exposed to a disproportionate amount of unhealthy food advertising. This study describes the nature and extent of food marketing on the Australian subscription television channels most popular with children. Advertisements broadcast on the six subscription television channels most popular with children were recorded over four days in February 2009. Advertised foods were coded as core/healthy, non-core/unhealthy or miscellaneous/other, and for persuasive marketing techniques (promotional characters, premium offers and nutrition claims). The majority of foods advertised were non-core (72%), with a mean rate of 0.7 non-core food advertisements broadcast per hour, per channel. The frequency of non-core food advertisements differed significantly across channels. Persuasive techniques were used to advertise non-core foods less frequently than core and miscellaneous foods. Non-core foods make up the majority of foods advertised on children's popular subscription channels. However, Australian children currently view less non-core food advertising on subscription television compared with free-to-air. Unlike free-to-air television, subscription services have the unique opportunity to limit inappropriate food marketing to children, given they are less reliant on advertising revenue. © 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia.

CERN GSM SUBSCRIPTIONS

CERN Multimedia

Labo Telecom

2002-01-01

AS Division has created a new EDH document for handling all GSM subscription requests and amendments. This procedure will enter force immediately and from now on the Labo Telecom stores will no longer be able to deal with requests submitted on paper forms. Detailed information on the subject can be found here and the Labo Telecom stores will continue to open every day between 11.00 a.m. and 12.00 midday. IT-CS-TEL, Labo Telecom

Regionally Selective Requirement for D[subscript 1]/D[subscript 5] Dopaminergic Neurotransmission in the Medial Prefrontal Cortex in Object-in-Place Associative Recognition Memory

Science.gov (United States)

Savalli, Giorgia; Bashir, Zafar I.; Warburton, E. Clea

2015-01-01

Object-in-place (OiP) memory is critical for remembering the location in which an object was last encountered and depends conjointly on the medial prefrontal cortex, perirhinal cortex, and hippocampus. Here we examined the role of dopamine D[subscript 1]/D[subscript 5] receptor neurotransmission within these brain regions for OiP memory. Bilateral…

Open Access, Library Subscriptions, and Article Processing Charges

KAUST Repository

Vijayakumar, J.K.

2016-05-01

Hybrid journals contains articles behind a pay-wall to be subscribed, as well as papers made open access when author pays article processing charge (APC). In such cases, an Institution will end up paying twice and Publishers tend to double-dip. Discussions and pilot models are emerging on pricing options, such as “offset pricing,” [where APCs are adjusted or discounted with subscription costs as vouchers or reductions in next year subscriptions, APCs beyond the subscription costs are modestly capped etc] and thus reduce Institutions’ cost. This presentation will explain different models available and how can we attain a transparent costing structure, where the scholarly community can feel the fairness in Publishers’ pricing mechanisms. Though most of the offset systems are developed through national level or consortium level negotiations, experience of individual institutions, like KAUST that subscribe to large e-journals collections, is important in making right decisions on saving Institutes costs and support openness in scholarly communications.

Open Access, Library Subscriptions, and Article Processing Charges

KAUST Repository

Vijayakumar, J.K.; Tamarkin, Molly

2016-01-01

Hybrid journals contains articles behind a pay-wall to be subscribed, as well as papers made open access when author pays article processing charge (APC). In such cases, an Institution will end up paying twice and Publishers tend to double-dip. Discussions and pilot models are emerging on pricing options, such as “offset pricing,” [where APCs are adjusted or discounted with subscription costs as vouchers or reductions in next year subscriptions, APCs beyond the subscription costs are modestly capped etc] and thus reduce Institutions’ cost. This presentation will explain different models available and how can we attain a transparent costing structure, where the scholarly community can feel the fairness in Publishers’ pricing mechanisms. Though most of the offset systems are developed through national level or consortium level negotiations, experience of individual institutions, like KAUST that subscribe to large e-journals collections, is important in making right decisions on saving Institutes costs and support openness in scholarly communications.

Vibrational Spectroscopy of the CCl[subscript 4] v[subscript 1] Mode: Theoretical Prediction of Isotopic Effects

Science.gov (United States)

Gaynor, James D.; Wetterer, Anna M.; Cochran, Rea M.; Valente, Edward J.; Mayer, Steven G.

2015-01-01

Raman spectroscopy is a powerful experimental technique, yet it is often missing from the undergraduate physical chemistry laboratory curriculum. Tetrachloromethane (CCl[subscript 4]) is the ideal molecule for an introductory vibrational spectroscopy experiment and the symmetric stretch vibration contains fine structure due to isotopic variations…

Prediction of VO[subscript 2]max in Children and Adolescents Using Exercise Testing and Physical Activity Questionnaire Data

Science.gov (United States)

Black, Nate E.; Vehrs, Pat R.; Fellingham, Gilbert W.; George, James D.; Hager, Ron

2016-01-01

Purpose: The purpose of this study was to evaluate the use of a treadmill walk-jog-run exercise test previously validated in adults and physical activity questionnaire data to estimate maximum oxygen consumption (VO[subscript 2]max) in boys (n = 62) and girls (n = 66) aged 12 to 17 years old. Methods: Data were collected from Physical Activity…

Effect of Eight Weekly Aerobic Training Program on Auditory Reaction Time and MaxVO[subscript 2] in Visual Impairments

Science.gov (United States)

Taskin, Cengiz

2016-01-01

The aim of study was to examine the effect of eight weekly aerobic exercises on auditory reaction time and MaxVO[subscript 2] in visual impairments. Forty visual impairment children that have blind 3 classification from the Turkey, experimental group; (age = 15.60 ± 1.10 years; height = 164.15 ± 4.88 cm; weight = 66.60 ± 4.77 kg) for twenty…

Data-Driven Transition: Joint Reporting of Subscription Expenditure and Publication Costs

Directory of Open Access Journals (Sweden)

Irene Barbers

2018-04-01

Full Text Available The transition process from the subscription model to the open access model in the world of scholarly publishing brings a variety of challenges to libraries. Within this evolving landscape, the present article takes a focus on budget control for both subscription and publication expenditure with the opportunity to enable the shift from one to the other. To reach informed decisions with a solid base of data to be used in negotiations with publishers, the diverse already-existing systems for managing publications costs and for managing journal subscriptions have to be adapted to allow comprehensive reporting on publication expenditure and subscription expenditure. In the case presented here, two separate systems are described and the establishment of joint reporting covering both these systems is introduced. Some of the results of joint reporting are presented as an example of how such a comprehensive monitoring can support management decisions and negotiations. On a larger scale, the establishment of the National Open Access Monitor in Germany is introduced, bringing together a diverse range of data from several already-existing systems, including, among others, holdings information, usage data, and data on publication fees. This system will enable libraries to access all relevant data with a single user interface.

Alterations in CNS Activity Induced by Botulinum Toxin Treatment in Spasmodic Dysphonia: An H[subscript 2][superscript 15]O PET Study

Science.gov (United States)

Ali, S. Omar; Thomassen, Michael; Schulz, Geralyn M.; Hosey, Lara A.; Varga, Mary; Ludlow, Christy L.; Braun, Allen R.

2006-01-01

Speech-related changes in regional cerebral blood flow (rCBF) were measured using H[subscript 2][superscript 15]O positron-emission tomography in 9 adults with adductor spasmodic dysphonia (ADSD) before and after botulinum toxin (BTX) injection and 10 age- and gender-matched volunteers without neurological disorders. Scans were acquired at rest…

47 CFR 73.642 - Subscription TV service.

Science.gov (United States)

2010-10-01

... expressed or implied, that: (1) Prevents or hinders it from rejecting or refusing any subscription TV..., service may be terminated. (ii) Charges, terms and conditions of service to subscribers must be applied... impositions of different sets of terms and conditions may be applied to subscribers in different...

mRNA and Protein Levels for GABA[subscript A][alpha]4, [alpha]5, [beta]1 and GABA[subscript B]R1 Receptors are Altered in Brains from Subjects with Autism

Science.gov (United States)

Fatemi, S. Hossein; Reutiman, Teri J.; Folsom, Timothy D.; Rooney, Robert J.; Patel, Diven H.; Thuras, Paul D.

2010-01-01

We have shown altered expression of gamma-aminobutyric acid A (GABA[subscript A]) and gamma-aminobutyric acid B (GABA[subscript B]) receptors in the brains of subjects with autism. In the current study, we sought to verify our western blotting data for GABBR1 via qRT-PCR and to expand our previous work to measure mRNA and protein levels of 3…

New types of subscriptions for CERN GSM

CERN Multimedia

IT Department

2010-01-01

A recent renegotiation of our commercial conditions with our mobile telephony operator allows us today to deploy new GSM mobile services, reduce communication costs, as well as put in place a new subscription system. First of all, the "email to SMS" service has already been extended to all Swiss numbers. This service allows you to send SMS messages (Short Message Service) to any Swiss mobile telephone from your CERN e-mail account. For further details, please refer to the web site http://cern.ch/sms. The sending of MMS messages (Multi-media Message Service) will be activated by default on all CERN subscriptions by the end of March 2010. This service allows users to attach to a text message an image, a video or an audio recording. All the necessary details for configuring this new service on CERN mobile phones will be published on the web site http://cern.ch/mms. Concerning mobile service costs, new rates have been put in place since 1st January 2010. All tariffs have dramatically decrea...

Anon-Pass: Practical Anonymous Subscriptions.

Science.gov (United States)

Lee, Michael Z; Dunn, Alan M; Katz, Jonathan; Waters, Brent; Witchel, Emmett

2013-12-31

We present the design, security proof, and implementation of an anonymous subscription service. Users register for the service by providing some form of identity, which might or might not be linked to a real-world identity such as a credit card, a web login, or a public key. A user logs on to the system by presenting a credential derived from information received at registration. Each credential allows only a single login in any authentication window, or epoch . Logins are anonymous in the sense that the service cannot distinguish which user is logging in any better than random guessing. This implies unlinkability of a user across different logins. We find that a central tension in an anonymous subscription service is the service provider's desire for a long epoch (to reduce server-side computation) versus users' desire for a short epoch (so they can repeatedly "re-anonymize" their sessions). We balance this tension by having short epochs, but adding an efficient operation for clients who do not need unlinkability to cheaply re-authenticate themselves for the next time period. We measure performance of a research prototype of our protocol that allows an independent service to offer anonymous access to existing services. We implement a music service, an Android-based subway-pass application, and a web proxy, and show that adding anonymity adds minimal client latency and only requires 33 KB of server memory per active user.

Group Theory and Crystal Field Theory: A Simple and Rigorous Derivation of the Spectroscopic Terms Generated by the t[subscript 2g][superscript 2] Electronic Configuration in a Strong Octahedral Field

Science.gov (United States)

Morpurgo, Simone

2007-01-01

The principles of symmetry and group theory are applied to the zero-order wavefunctions associated with the strong-field t[subscript 2g][superscript 2] configuration and their symmetry-adapted linear combinations (SALC) associated with the generated energy terms are derived. This approach will enable students to better understand the use of…

An Experiment Illustrating the Change in Ligand p"K"[subscript a] upon Protein Binding

Science.gov (United States)

Chenprakhon, Pirom; Panijpan, Bhinyo; Chaiyen, Pimchai

2012-01-01

The modulation of ligand p"K"[subscript a] due to its surrounding environment is a crucial feature that controls many biological phenomena. For example, the shift in the p"K"[subscript a] of substrates or catalytic residues at enzyme active sites upon substrate binding often triggers and controls enzymatic reactions. In this work, we developed an…

The Effects of the Rope Jump Training Program in Physical Education Lessons on Strength, Speed and VO[subscript 2] Max in Children

Science.gov (United States)

Eler, Nebahat; Acar, Hakan

2018-01-01

The aim of this study is to examine the effects of rope-jump training program in physical education lessons on strength, speed and VO[subscript 2] max in 10-12 year old boys. 240 male students; rope-jump group (n = 120) and control group (n = 120) participated in the study. Rope-Jump group continued 10 weeks of regular physical education and sport…

CERN Library - Reduction of subscriptions to scientific journals

CERN Multimedia

2005-01-01

The Library Working Group for Acquisitions has identified some scientific journal subscriptions as candidates for cancellation. Although the 2005 budget is unchanged with respect to 2004 thanks to the efforts of the Management, it does not take account of inflation, which for many years has been much higher for scientific literature than the normal cost-of-living index. For 2006, the inflation rate is estimated to be 7-8%. Moreover, the Library does not only intend to compensate for the loss of purchasing power but also to make available some funds to promote new Open Access publishing models. (See Bulletin No.15/2005) The list of candidates can be found on the Library homepage (http://library.cern.ch/). In addition, some subscriptions will be converted to online-only, i.e. CERN will no longer order the print version of certain journals. We invite users to carefully check the list (http://library.cern.ch/). Comments on this proposal should be sent to the WGA Chairman, Rudiger Voss, with a copy to the Hea...

31 CFR 344.8 - What other provisions apply to subscriptions for Demand Deposit securities?

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false What other provisions apply to subscriptions for Demand Deposit securities? 344.8 Section 344.8 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) FISCAL SERVICE, DEPARTMENT OF THE TREASURY BUREAU OF THE PUBLIC...

price list 2015.pdf | Subscription | Journals | Resources | public ...

Indian Academy of Sciences (India)

Home; public; Resources; Journals; Subscription; price list 2015.pdf. 404! error. The page your are looking for can not be found! Please check the link or use the navigation bar at the top. YouTube; Twitter; Facebook; Blog. Academy News. IAS Logo. 29th Mid-year meeting. Posted on 19 January 2018. The 29th Mid-year ...

How Einstein Discovered "E[subscript 0] = mc[squared]"

Science.gov (United States)

Hecht, Eugene

2012-01-01

This paper traces Einstein's discovery of "the equivalence of mass [m] and energy ["E[subscript 0]"]." He came to that splendid insight in 1905 while employed by the Bern Patent Office, at which time he was not an especially ardent reader of physics journals. How then did the young savant, working outside of academia in semi-isolation, realize…

29 CFR 1905.7 - Form of documents; subscription; copies.

Science.gov (United States)

2010-07-01

... UNDER THE WILLIAMS-STEIGER OCCUPATIONAL SAFETY AND HEALTH ACT OF 1970 General § 1905.7 Form of documents... 29 Labor 5 2010-07-01 2010-07-01 false Form of documents; subscription; copies. 1905.7 Section 1905.7 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION...

The Essence Of Government Shares Subscription A Review The Implementation Of State-Owned Enterprises

Directory of Open Access Journals (Sweden)

Urbanisasi

2015-08-01

Full Text Available The purpose of this study was to determine and explain the mechanisms and the implementation of government share subscription in the implementation of SOEs legal standing of the government shares subscription in the implementation of the state budget that separated in the implementation of SOEs and its legal implications of state loss or not and also legal accountability for losses arising out of shares subscription of SOE. In this study the authors use normative legal research. The data obtained in this study will be analyzed using qualitative normative method with inductive logic. Results from the study indicate that state shares subscription in the establishment of SOE or limited company with funds derived from State Budget are separated. Thus the government no longer has any authority in the field of civil law as a business entity. A clear separation of the status of country as business and as government organizer carries consequences. With the separation then there is clarity about the concept of the state financial losses. SOE as one form of business entity that aim to make a profit is a separate legal entity and has responsibilities that are separately anyway though formed and capital originating from the state finances and the loss of one transaction or in legal entity cannot be categorized as a state finance loss because the state has functioned as a private legal entity.

GRID[subscript C] Renewable Energy Data Streaming into Classrooms

Science.gov (United States)

DeLuca, V. William; Carpenter, Pam; Lari, Nasim

2010-01-01

For years, researchers have shown the value of using real-world data to enhance instruction in mathematics, science, and social studies. In an effort to help develop students' higher-order thinking skills in a data-rich learning environment, Green Research for Incorporating Data in the Classroom (GRID[subscript C]), a National Science…

Killeen's (2005) "p[subscript rep]" Coefficient: Logical and Mathematical Problems

Science.gov (United States)

Maraun, Michael; Gabriel, Stephanie

2010-01-01

In his article, "An Alternative to Null-Hypothesis Significance Tests," Killeen (2005) urged the discipline to abandon the practice of "p[subscript obs]"-based null hypothesis testing and to quantify the signal-to-noise characteristics of experimental outcomes with replication probabilities. He described the coefficient that he…

Blockade of IP[subscript 3]-Mediated SK Channel Signaling in the Rat Medial Prefrontal Cortex Improves Spatial Working Memory

Science.gov (United States)

Brennan, Avis R.; Dolinsky, Beth; Vu, Mai-Anh T.; Stanley, Marion; Yeckel, Mark F.; Arnsten, Amy F. T.

2008-01-01

Planning and directing thought and behavior require the working memory (WM) functions of prefrontal cortex. WM is compromised by stress, which activates phosphatidylinositol (PI)-mediated IP[subscript 3]-PKC intracellular signaling. PKC overactivation impairs WM operations and in vitro studies indicate that IP[subscript 3] receptor (IP[subscript…

Measurement of Levitation Forces of High-"T[subscript c] Superconductors

Science.gov (United States)

Becker, M.; Koblischka, M. R.; Hartmann, U.

2010-01-01

We show the construction of a so-called levitation balance which is capable of measuring the levitation forces between a permanent magnet and a superconducting high-T[subscript c] thin film sample. The underlying theoretical basis is discussed in detail. The experiment is performed as an introductory physics experiment for school students as well…

Extending the Mertonian Norms: Scientists' Subscription to Norms of Research

Science.gov (United States)

Anderson, Melissa S.; Ronning, Emily A.; De Vries, Raymond; Martinson, Brian C.

2010-01-01

This analysis, based on focus groups and a national survey, assesses scientists' subscription to the Mertonian norms of science and associated counternorms. It also supports extension of these norms to governance (as opposed to administration), as a norm of decision-making, and quality (as opposed to quantity), as an evaluative norm. (Contains 1…

[Reading behavior and preferences regarding subscriptions to scientific journals : Results of a survey of members of the German Society for General and Visceral Surgery].

Science.gov (United States)

Ronellenfitsch, U; Klinger, C; Buhr, H J; Post, S

2015-11-01

The purpose of surgical literature is to publish the latest study results and to provide continuing medical education to readers. For optimal allocation of resources, institutional subscribers, professional societies and scientific publishers require structured data on reading and subscription preferences of potential readers of surgical literature. To obtain representative data on the preferences of German general and visceral surgeons regarding reading of and subscription to scientific journals. All members of the German Society for General and Visceral Surgery (DGAV) were invited to participate in a web-based survey. Questions were asked on the affiliation and position of the member, individual journal subscriptions, institutional access to scientific journals, preferences regarding electronic or print articles and special subscriptions for society members. Answers were descriptively analyzed. A total of 630 out of 4091 (15 %) members participated in the survey and 73 % of the respondents had at least 1 individual subscription to a scientific journal. The most frequently subscribed journal was Der Chirurg (47 % of respondents). The institutional access to journals was deemed insufficient by 48 % of respondents, predominantly in primary care hospitals and outpatient clinics. Almost half of the respondents gave sufficient importance to reading printed versions of articles for which they would pay extra fees. A group subscription for society members was perceived as advantageous as long as no relevant extra costs were incurred. This structured survey among members of the DGAV provides data on preferences regarding reading of and subscription to scientific journals. Individual subscriptions to journals are still common, possibly due to suboptimal institutional access particularly at smaller non-academic institutions. In an age of online publications it seems surprising that many respondents place a high value on printed versions. The results are relevant for

Health sciences libraries' subscriptions to journals: expectations of general practice departments and collection-based analysis.

Science.gov (United States)

Barreau, David; Bouton, Céline; Renard, Vincent; Fournier, Jean-Pascal

2018-04-01

The aims of this study were to (i) assess the expectations of general practice departments regarding health sciences libraries' subscriptions to journals and (ii) describe the current general practice journal collections of health sciences libraries. A cross-sectional survey was distributed electronically to the thirty-five university general practice departments in France. General practice departments were asked to list ten journals to which they expected access via the subscriptions of their health sciences libraries. A ranked reference list of journals was then developed. Access to these journals was assessed through a survey sent to all health sciences libraries in France. Adequacy ratios (access/need) were calculated for each journal. All general practice departments completed the survey. The total reference list included 44 journals. This list was heterogeneous in terms of indexation/impact factor, language of publication, and scope (e.g., patient care, research, or medical education). Among the first 10 journals listed, La Revue Prescrire (96.6%), La Revue du Praticien-Médecine Générale (90.9%), the British Medical Journal (85.0%), Pédagogie Médicale (70.0%), Exercer (69.7%), and the Cochrane Database of Systematic Reviews (62.5%) had the highest adequacy ratios, whereas Family Practice (4.2%), the British Journal of General Practice (16.7%), Médecine (29.4%), and the European Journal of General Practice (33.3%) had the lowest adequacy ratios. General practice departments have heterogeneous expectations in terms of health sciences libraries' subscriptions to journals. It is important for librarians to understand the heterogeneity of these expectations, as well as local priorities, so that journal access meets users' needs.

How Often Is p[subscript rep] Close to the True Replication Probability?

Science.gov (United States)

Trafimow, David; MacDonald, Justin A.; Rice, Stephen; Clason, Dennis L.

2010-01-01

Largely due to dissatisfaction with the standard null hypothesis significance testing procedure, researchers have begun to consider alternatives. For example, Killeen (2005a) has argued that researchers should calculate p[subscript rep] that is purported to indicate the probability that, if the experiment in question were replicated, the obtained…

11 CFR 100.52 - Gift, subscription, loan, advance or deposit of money.

Science.gov (United States)

2010-01-01

... money. 100.52 Section 100.52 Federal Elections FEDERAL ELECTION COMMISSION GENERAL SCOPE AND DEFINITIONS..., advance or deposit of money. (a) A gift, subscription, loan (except for a loan made in accordance with 11 CFR 100.72 and 100.73), advance, or deposit of money or anything of value made by any person for the...

Role of library's subscription licenses in promoting open access to scientific research

KAUST Repository

Buck, Stephen

2018-01-01

This presentation, based on KAUST’’s experience to date, will attempt to explain the different ways of bringing Open Access models to scientific Publisher’s licenses. Our dual approach with offset pricing is to redirect subscription money to publishing money and embed green open access deposition terms in understandable language in our license agreements. Resolving the inherent complexities in open access publishing, repository depositions and offsetting models will save libraries money and also time wasted on tedious and unnecessary administration work. Researchers will also save their time with overall clarity and transparency. This will enable trust and, where mistakes are made, and there inevitably will be with untried models, we can learn from these mistakes and make better, more robust services with auto deposition of our articles to our repository fed by Publishers’ themselves. The plan is to cover all Publishers with OA license terms for KAUST author’s right while continuing our subscription to them. There are marketing campaigns, awareness sessions are planned, in addition to establishing Libguides to help researchers, in addition to manage offset pricing models.

Role of library's subscription licenses in promoting open access to scientific research

KAUST Repository

Buck, Stephen

2018-04-30

This presentation, based on KAUST’’s experience to date, will attempt to explain the different ways of bringing Open Access models to scientific Publisher’s licenses. Our dual approach with offset pricing is to redirect subscription money to publishing money and embed green open access deposition terms in understandable language in our license agreements. Resolving the inherent complexities in open access publishing, repository depositions and offsetting models will save libraries money and also time wasted on tedious and unnecessary administration work. Researchers will also save their time with overall clarity and transparency. This will enable trust and, where mistakes are made, and there inevitably will be with untried models, we can learn from these mistakes and make better, more robust services with auto deposition of our articles to our repository fed by Publishers’ themselves. The plan is to cover all Publishers with OA license terms for KAUST author’s right while continuing our subscription to them. There are marketing campaigns, awareness sessions are planned, in addition to establishing Libguides to help researchers, in addition to manage offset pricing models.

Does SDDS Subscription Reduce Borrowing Costs for Emerging Market Economies?

OpenAIRE

John Cady

2005-01-01

Does macroeconomic data transparency-as signaled by subscription to the IMF's Special Data Dissemination Standard (SDDS)-help reduce borrowing costs in international capital markets? This question is examined using data on new issues of sovereign foreign-currency-denominated (U.S. dollar, yen, and euro) bonds for several emerging market economies. Panel econometric estimates indicate that spreads on new bond issues declined on average by close to 20 percent, or by an average of about 55 basis...

Incorporating Learning Motivation and Self-Concept in Mathematical Communicative Ability

Science.gov (United States)

Rajagukguk, Waminton

2016-01-01

This research is trying to determine of the mathematical concepts, instead by integrating the learning motivation (X[subscript 1]) and self-concept (X[subscript 2]) can contribute to the mathematical communicative ability (Y). The test instruments showed the following results: (1) simple regressive equation Y on X[subscript 1] was Y = 32.891 +…

Effects of spatial location and household wealth on health insurance subscription among women in Ghana.

Science.gov (United States)

Kumi-Kyereme, Akwasi; Amo-Adjei, Joshua

2013-06-17

This study compares ownership of health insurance among Ghanaian women with respect to wealth status and spatial location. We explore the overarching research question by employing geographic and proxy means targeting through interactive analysis of wealth status and spatial issues. The paper draws on the 2008 Ghana Demographic and Health Survey. Bivariate descriptive analysis coupled with binary logistic regression estimation technique was used to analyse the data. By wealth status, the likelihood of purchasing insurance was significantly higher among respondents from the middle, richer and richest households compared to the poorest (reference category) and these differences widened more profoundly in the Northern areas after interacting wealth with zone of residence. Among women at the bottom of household wealth (poorest and poorer), there were no statistically significant differences in insurance subscription in all the areas. The results underscore the relevance of geographic and proxy means targeting in identifying populations who may be need of special interventions as part of the efforts to increase enrolment as well as means of social protection against the vulnerable.

Operational Experience of an Open-Access, Subscription-Based Mass Spectrometry and Proteomics Facility

Science.gov (United States)

Williamson, Nicholas A.

2018-03-01

This paper discusses the successful adoption of a subscription-based, open-access model of service delivery for a mass spectrometry and proteomics facility. In 2009, the Mass Spectrometry and Proteomics Facility at the University of Melbourne (Australia) moved away from the standard fee for service model of service provision. Instead, the facility adopted a subscription- or membership-based, open-access model of service delivery. For a low fixed yearly cost, users could directly operate the instrumentation but, more importantly, there were no limits on usage other than the necessity to share available instrument time with all other users. All necessary training from platform staff and many of the base reagents were also provided as part of the membership cost. These changes proved to be very successful in terms of financial outcomes for the facility, instrument access and usage, and overall research output. This article describes the systems put in place as well as the overall successes and challenges associated with the operation of a mass spectrometry/proteomics core in this manner. [Figure not available: see fulltext.

Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model.

Science.gov (United States)

Reiser, Leonore; Berardini, Tanya Z; Li, Donghui; Muller, Robert; Strait, Emily M; Li, Qian; Mezheritsky, Yarik; Vetushko, Andrey; Huala, Eva

2016-01-01

Databases and data repositories provide essential functions for the research community by integrating, curating, archiving and otherwise packaging data to facilitate discovery and reuse. Despite their importance, funding for maintenance of these resources is increasingly hard to obtain. Fueled by a desire to find long term, sustainable solutions to database funding, staff from the Arabidopsis Information Resource (TAIR), founded the nonprofit organization, Phoenix Bioinformatics, using TAIR as a test case for user-based funding. Subscription-based funding has been proposed as an alternative to grant funding but its application has been very limited within the nonprofit sector. Our testing of this model indicates that it is a viable option, at least for some databases, and that it is possible to strike a balance that maximizes access while still incentivizing subscriptions. One year after transitioning to subscription support, TAIR is self-sustaining and Phoenix is poised to expand and support additional resources that wish to incorporate user-based funding strategies. Database URL: www.arabidopsis.org. © The Author(s) 2016. Published by Oxford University Press.

Teaching Molecular Symmetry of Dihedral Point Groups by Drawing Useful 2D Projections

Science.gov (United States)

Chen, Lan; Sun, Hongwei; Lai, Chengming

2015-01-01

There are two main difficulties in studying molecular symmetry of dihedral point groups. One is locating the C[subscript 2] axes perpendicular to the C[subscript n] axis, while the other is finding the s[subscript]d planes which pass through the C[subscript n] axis and bisect the angles formed by adjacent C[subscript 2] axes. In this paper, a…

Rethinking the Subscription Paradigm for Journals: Using Interlibrary Loan in Collection Development for Serials

Science.gov (United States)

Barton, Gail Perkins; Relyea, George E.; Knowlton, Steven A.

2018-01-01

Many librarians evaluate local Interlibrary Loan (ILL) statistics as part of collection development decisions concerning new subscriptions. In this study, the authors examine whether the number of ILL article requests received in one academic year can predict the use of those same journal titles once they are added as library resources. There is…

Magazine "Companion Websites" and the Demand for Newsstand Sales and Subscriptions

DEFF Research Database (Denmark)

Kaiser, Ulrich; Kongsted, H.C.

2012-01-01

analysis finds some support for the widespread belief that the Internet cannibalizes print media. On average, a 1% increase in companion website traffic is associated with a weakly significant decrease in total print circulation by 0.15%. This association is mainly driven by a statistically significant...... and negative mapping between website visits and kiosk sales, although they do not find any statistically significant relationship between website visits and subscriptions. The latter finding is reassuring for publishers because advertisers value a large subscriber base. Moreover, the authors show...

Peer review quality and transparency of the peer-review process in open access and subscription journals

NARCIS (Netherlands)

Wicherts, J.M.

2016-01-01

Background Recent controversies highlighting substandard peer review in Open Access (OA) and traditional (subscription) journals have increased the need for authors, funders, publishers, and institutions to assure quality of peer-review in academic journals. I propose that transparency of the

Declining subscriptions to the Maliando Mutual Health Organisation in Guinea-Conakry (West Africa): what is going wrong?

Science.gov (United States)

Criel, Bart; Waelkens, Maria Pia

2003-10-01

Mutual Health Organisations (MHOs) are a type of community health insurance scheme that are being developed and promoted in sub-Saharan Africa. In 1998, an MHO was organised in a rural district of Guinea to improve access to quality health care. Households paid an annual insurance fee of about US$2 per individual. Contributions were voluntary. The benefit package included free access to all first line health care services (except for a small co-payment), free paediatric care, free emergency surgical care and free obstetric care at the district hospital. Also included were part of the cost of emergency transport to the hospital. In 1998, the MHO covered 8% of the target population, but, by 1999, the subscription rate had dropped to about 6%. In March 2000, focus groups were held with members and non-members of the scheme to find out why subscription rates were so low. The research indicated that a failure to understand the scheme does not explain these low rates. On the contrary, the great majority of research subjects, members and non-members alike, acquired a very accurate understanding of the concepts and principles underlying health insurance. They value the system's re-distributive effects, which goes beyond household, next of kin or village. The participants accurately point out the sharp differences that exist between traditional financial mechanisms and the principle of health insurance, as well as the advantages and disadvantages of both. The ease with which risk-pooling is accepted as a financial mechanism which addresses specific needs demonstrates that it is not, per se, necessary to build health insurance schemes on existing or traditional systems of mutual aid. The majority of the participants consider the individual premium of 2 US dollars to be fair. There is, however, a problem of affordability for many poor and/or large families who cannot raise enough money to pay the subscription for all household members in one go. However, the main reason for

Piracy versus Netflix : Subscription Video on Demand Dissatisfaction as an Antecedent of Piracy

OpenAIRE

Riekkinen, Janne

2018-01-01

Drawing from cognitive dissonance and neutralization theories, this study seeks to improve the understanding on consumer decision-making between the current legal and illegal video consumption alternatives. We develop and test a research model featuring Subscription Video on Demand (SVOD) satisfaction and various dimensions of SVOD quality as antecedents of video piracy neutralizations and attitudes. Based on results from an online survey among Finnish SVOD users, SVOD satisfaction is primari...

Dissonance and Neutralization of Subscription Streaming Era Digital Music Piracy : An Initial Exploration

OpenAIRE

Riekkinen, Janne

2016-01-01

Both legal and illegal forms of digital music consumption continue to evolve with wider adoption of subscription streaming services. With this paper, we aim to extend theory on digital music piracy by showing that the rising controversy and diminishing acceptance of illegal forms of consumption call for new theoretical components and interactions. We introduce a model that integrates insights from neutralization and cognitive dissonance theories. As an initial empirical test of th...

Perception of quality of health delivery and health insurance subscription in Ghana.

Science.gov (United States)

Amo-Adjei, Joshua; Anku, Prince Justin; Amo, Hannah Fosuah; Effah, Mavis Osei

2016-07-29

National health insurance schemes (NHIS) in developing countries and perhaps in developed countries as well is a considered a pro-poor intervention by helping to bridge the financial burden of access to quality health care. Perceptions of quality of health service could have immense impacts on enrolment. This paper shows how perception of service quality under Ghana's insurance programme contributes to health insurance subscription. The study used the 2014 Ghana Demographic and Health Survey (GDHS) dataset. Both descriptive proportions and binary logistic regression techniques were applied to generate results that informed the discussion. Our results show that a high proportion of females (33 %) and males (35 %) felt that the quality of health provided to holders of the NHIS card was worse. As a result, approximately 30 % of females and 22%who perceived health care as worse by holding an insurance card did not own an insurance policy. While perceptions of differences in quality among females were significantly different (AOR = 0.453 [95 % CI = 0.375, 0.555], among males, the differences in perceptions of quality of health services under the NHIS were independent in the multivariable analysis. Beyond perceptions of quality, being resident in the Upper West region was an important predictor of health insurance ownership for both males and females. For such a social and pro-poor intervention, investing in quality of services to subscribers, especially women who experience enormous health risks in the reproductive period can offer important gains to sustaining the scheme as well as offering affordable health services.

Bringing together the work of subscription and open access specialists: challenges and changes at the University of Sussex

Directory of Open Access Journals (Sweden)

Eleanor Craig

2017-03-01

Full Text Available The rise in open access (OA publishing has required library staff across many UK academic institutions to take on new roles and responsibilities to support academics. At the same time, the long-established work of negotiating with publishers around journal subscriptions is changing as such deals now usually include OA payment or discount plans in many different forms that vary from publisher to publisher. This article outlines some of the issues we encountered at the University of Sussex Library whilst trying to pull together the newer strand of OA advocacy and funder compliance work with existing responsibilities for managing subscription deals. It considers the challenges faced in effectively bringing together Library staff with knowledge in these areas, and outlines the steps we have taken so far to ensure OA publishing is taken into account wherever appropriate.

Effects of GABA[subscript A] Modulators on the Repeated Acquisition of Response Sequences in Squirrel Monkeys

Science.gov (United States)

Campbell, Una C.; Winsauer, Peter J.; Stevenson, Michael W.; Moerschbaecher, Joseph M.

2004-01-01

The present study investigated the effects of positive and negative GABA[subscript A] modulators under three different baselines of repeated acquisition in squirrel monkeys in which the monkeys acquired a three-response sequence on three keys under a second-order fixed-ratio (FR) schedule of food reinforcement. In two of these baselines, the…

The A[subscript 1c] Blood Test: An Illustration of Principles from General and Organic Chemistry

Science.gov (United States)

Kerber, Robert C.

2007-01-01

The glycated hemoglobin blood test, usually designated as the A[subscript 1c] test, is a key measure of the effectiveness of glucose control in diabetics. The chemistry of glucose in the bloodstream, which underlies the test and its impact, provides an illustration of the importance of chemical equilibrium and kinetics to a major health problem.…

A regression approach for Zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Peer Review Quality and Transparency of the Peer-Review Process in Open Access and Subscription Journals.

Science.gov (United States)

Wicherts, Jelte M

2016-01-01

Recent controversies highlighting substandard peer review in Open Access (OA) and traditional (subscription) journals have increased the need for authors, funders, publishers, and institutions to assure quality of peer-review in academic journals. I propose that transparency of the peer-review process may be seen as an indicator of the quality of peer-review, and develop and validate a tool enabling different stakeholders to assess transparency of the peer-review process. Based on editorial guidelines and best practices, I developed a 14-item tool to rate transparency of the peer-review process on the basis of journals' websites. In Study 1, a random sample of 231 authors of papers in 92 subscription journals in different fields rated transparency of the journals that published their work. Authors' ratings of the transparency were positively associated with quality of the peer-review process but unrelated to journal's impact factors. In Study 2, 20 experts on OA publishing assessed the transparency of established (non-OA) journals, OA journals categorized as being published by potential predatory publishers, and journals from the Directory of Open Access Journals (DOAJ). Results show high reliability across items (α = .91) and sufficient reliability across raters. Ratings differentiated the three types of journals well. In Study 3, academic librarians rated a random sample of 140 DOAJ journals and another 54 journals that had received a hoax paper written by Bohannon to test peer-review quality. Journals with higher transparency ratings were less likely to accept the flawed paper and showed higher impact as measured by the h5 index from Google Scholar. The tool to assess transparency of the peer-review process at academic journals shows promising reliability and validity. The transparency of the peer-review process can be seen as an indicator of peer-review quality allowing the tool to be used to predict academic quality in new journals.

The Role of Subscription-Based Patrol and Restitution in the Future of Liberty

Directory of Open Access Journals (Sweden)

Gil Guillory

2009-02-01

Full Text Available Market anarchists are often keen to know how we might rid ourselves of the twin evils institutionalized in the state: taxation and monopoly. A possible future history for North America is suggested, focusing upon the implications of the establishment of a subscription-based patrol and restitution business sector. We favor Rothbard over Higgs regarding crises and liberty. We favor Barnett over Rothbard regarding vertical integration of security. We examine derived demand for adjudication, mediation and related goods; and we advance the thesis that private adjudication will tend to libertarianly just decisions. We show how firms will actively build civil society, strengthening and coordinating Nisbettian intermediating institutions.

Promoting public transport as a subscription service: Effects of a free month travel card

DEFF Research Database (Denmark)

Thøgersen, John

2009-01-01

Newspapers, book clubs, telephone services and many other subscription services are often marketed to new customers by means of a free or substantially discounted trial period. This article evaluates this method as a means to promote commuting by public transport in a field experiment and based...... that had an effect was the free month travel card, which led to a significant increase in commuting by public transport.As expected, the effect was mediated through a change in behavioural intentions rather than a change in perceived constraints. As expected, the effect became weaker when the promotion...

Elliptical Orbit [arrow right] 1/r[superscript 2] Force

Science.gov (United States)

Prentis, Jeffrey; Fulton, Bryan; Hesse, Carol; Mazzino, Laura

2007-01-01

Newton's proof of the connection between elliptical orbits and inverse-square forces ranks among the "top ten" calculations in the history of science. This time-honored calculation is a highlight in an upper-level mechanics course. It would be worthwhile if students in introductory physics could prove the relation "elliptical orbit" [arrow right]…

A regression approach for zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

Using the "K[subscript 5]Connected Cognition Diagram" to Analyze Teachers' Communication and Understanding of Regions in Three-Dimensional Space

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Moore-Russo, Deborah; Viglietti, Janine M.

2012-01-01

This paper reports on a study that introduces and applies the "K[subscript 5]Connected Cognition Diagram" as a lens to explore video data showing teachers' interactions related to the partitioning of regions by axes in a three-dimensional geometric space. The study considers "semiotic bundles" (Arzarello, 2006), introduces "semiotic connections,"…

Retro-regression--another important multivariate regression improvement.

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Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Gender Differences in the Importance of Personality Traits in Predicting Leadership Self-Efficacy

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Huszczo, Gregory; Endres, Megan Lee

2017-01-01

Our goal in this study was to investigate antecedents to formation of leadership self-efficacy (LSE) for men versus women. We used a relative importance analysis, which allows more precise identification of important predictors without concern of multicollinearity effects on R[superscript 2]. Using a sample of 325 business students, we found that…

Experimental Determination of pK[subscript a] Values and Metal Binding for Biomolecular Compounds Using [superscript 31]P NMR Spectroscopy

Science.gov (United States)

Swartz, Mason A.; Tubergen, Philip J.; Tatko, Chad D.; Baker, Rachael A.

2018-01-01

This lab experiment uses [superscript 31]P NMR spectroscopy of biomolecules to determine pK[subscript a] values and the binding energies of metal/biomolecule complexes. Solutions of adenosine nucleotides are prepared, and a series of [superscript 31]P NMR spectra are collected as a function of pH and in the absence and presence of magnesium or…

Muscle Dysmorphia, Gender Role Stress, and Sociocultural Influences: An Exploratory Study

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Readdy, Tucker; Watkins, Patti Lou; Cardinal, Bradley J.

2011-01-01

Our study explored the contribution of gender role stress (GRS) and sociocultural appearance demands to symptoms of muscle dysmorphia (MD) in a college sample of 219 women and 154 men. For women, five GRS subscales, sociocultural appearance demands, age, and frequency of aerobic exercise predicted MD symptoms (model R[superscript 2] = 0.33;…

The GABA[subscript A] Receptor Agonist Muscimol Induces an Age- and Region-Dependent Form of Long-Term Depression in the Mouse Striatum

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Zhang, Xiaoqun; Yao, Ning; Chergui, Karima

2016-01-01

Several forms of long-term depression (LTD) of glutamatergic synaptic transmission have been identified in the dorsal striatum and in the nucleus accumbens (NAc). Such experience-dependent synaptic plasticity might play important roles in reward-related learning. The GABA[subscript A] receptor agonist muscimol was recently found to trigger a…

Weighted linear regression using D2H and D2 as the independent variables

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Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

A Pictorial Visualization of Normal Mode Vibrations of the Fullerene (C[subscript 60]) Molecule in Terms of Vibrations of a Hollow Sphere

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Dunn, Janette L.

2010-01-01

Understanding the normal mode vibrations of a molecule is important in the analysis of vibrational spectra. However, the complicated 3D motion of large molecules can be difficult to interpret. We show how images of normal modes of the fullerene molecule C[subscript 60] can be made easier to understand by superimposing them on images of the normal…

PI[subscript 3]-Kinase Cascade Has a Differential Role in Acquisition and Extinction of Conditioned Fear Memory in Juvenile and Adult Rats

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Slouzkey, Ilana; Maroun, Mouna

2016-01-01

The basolateral amygdala (BLA), medial prefrontal cortex (mPFC) circuit, plays a crucial role in acquisition and extinction of fear memory. Extinction of aversive memories is mediated, at least in part, by the phosphoinositide-3 kinase (P[subscript 3]K)/Akt pathway in adult rats. There is recent interest in the neural mechanisms that mediate fear…

Faculty Decisions on Serials Subscriptions Differ Significantly from Decisions Predicted by a Bibliometric Tool.

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Sue F. Phelps

2016-03-01

Full Text Available Objective – To compare faculty choices of serials subscription cancellations to the scores of a bibliometric tool. Design – Natural experiment. Data was collected about faculty valuations of serials. The California Digital Library Weighted Value Algorithm (CDL-WVA was used to measure the value of journals to a particular library. These two sets of scores were then compared. Setting – A public research university in the United States of America. Subjects – Teaching and research faculty, as well as serials data. Methods – Experimental methodology was used to compare faculty valuations of serials (based on their journal cancellation choices to bibliometric valuations of the same journal titles (determined by CDL-WVA scores to identify the match rate between the faculty choices and the bibliographic data. Faculty were asked to select titles to cancel that totaled approximately 30% of the budget for their disciplinary fund code. This “keep” or “cancel” choice was the binary variable for the study. Usage data was gathered for articles downloaded through the link resolver for titles in each disciplinary dataset, and the CDL-WVA scores were determined for each journal title based on utility, quality, and cost effectiveness. Titles within each dataset were ranked highest to lowest using the CDL-WVA scores within each fund code, and then by subscription cost for titles with the same CDL-WVA score. The journal titles selected for comparison were those that ranked above the approximate 30% of titles chosen for cancellation by faculty and CDL-WVA scores. Researchers estimated an odds ratio of faculty choosing to keep a title and a CDL-WVA score that indicated the title should be kept. The p-value for that result was less than 0.0001, indicating that there was a negligible probability that the results were by chance. They also applied logistic regression to quantify the association between the numeric score of CDL-WVA and the binary variable

The microcomputer scientific software series 2: general linear model--regression.

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Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

Science.gov (United States)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

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Li Jian

2017-01-01

Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

Zeolite 5A Catalyzed Etherification of Diphenylmethanol

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Cooke, Jason; Henderson, Eric J.; Lightbody, Owen C.

2009-01-01

An experiment for the synthetic undergraduate laboratory is described in which zeolite 5A catalyzes the room temperature dehydration of diphenylmethanol, (C[subscript 6]H[subscript 5])[subscript 2]CHOH, producing 1,1,1',1'-tetraphenyldimethyl ether, (C[subscript 6]H[subscript 5])[subscript 2]CHOCH(C[subscript 6]H[subscript 5])[subscript 2]. The…

Combining Sustainable Synthesis of a Versatile Ruthenium Dihydride Complex with Structure Determination Using Group Theory and Spectroscopy

Science.gov (United States)

Armstrong, Christopher; Burnham, Jennifer A. J.; Warminski, Edward E.

2017-01-01

A good-yielding two-step synthesis of RuH[subscript 2](CO)(PPh[subscript 3])[subscript 3] using conventional or microwave-assisted reflux techniques is described for use in undergraduate teaching laboratories. RuH[subscript 2](CO)(PPh[subscript 3])[subscript 3] is synthesized from RuCl[subscript 3]·xH[subscript 2]O, PPh[subscript 3], and KOH in…

Use of the "gl1" Mutant and the "CA-rop2" Transgenic Plants of "Arabidopsis thaliana" in the Biology Laboratory Course

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Zheng, Zhi-Liang

2006-01-01

This article describes the use of the "glabrous1 (g11)" mutant and constitutively active "(CA)-rop2" transgenic plants of "Arabidopsis thaliana" in teaching genetics laboratory for both high school and undergraduate students. The experiments provide students with F[subscript 1] and F[subscript 2] generations within a semester for genetic and…

A prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2, based on simple clinical parameters.

Science.gov (United States)

Koeneman, Margot M; van Lint, Freyja H M; van Kuijk, Sander M J; Smits, Luc J M; Kooreman, Loes F S; Kruitwagen, Roy F P M; Kruse, Arnold J

2017-01-01

This study aims to develop a prediction model for spontaneous regression of cervical intraepithelial neoplasia grade 2 (CIN 2) lesions based on simple clinicopathological parameters. The study was conducted at Maastricht University Medical Center, the Netherlands. The prediction model was developed in a retrospective cohort of 129 women with a histologic diagnosis of CIN 2 who were managed by watchful waiting for 6 to 24months. Five potential predictors for spontaneous regression were selected based on the literature and expert opinion and were analyzed in a multivariable logistic regression model, followed by backward stepwise deletion based on the Wald test. The prediction model was internally validated by the bootstrapping method. Discriminative capacity and accuracy were tested by assessing the area under the receiver operating characteristic curve (AUC) and a calibration plot. Disease regression within 24months was seen in 91 (71%) of 129 patients. A prediction model was developed including the following variables: smoking, Papanicolaou test outcome before the CIN 2 diagnosis, concomitant CIN 1 diagnosis in the same biopsy, and more than 1 biopsy containing CIN 2. Not smoking, Papanicolaou class predictive of disease regression. The AUC was 69.2% (95% confidence interval, 58.5%-79.9%), indicating a moderate discriminative ability of the model. The calibration plot indicated good calibration of the predicted probabilities. This prediction model for spontaneous regression of CIN 2 may aid physicians in the personalized management of these lesions. Copyright © 2016 Elsevier Inc. All rights reserved.

Locational Prices in Capacity Subscription Market Considering Transmission Limitations

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S. Babaeinejad Sarookolaee

2013-06-01

Full Text Available This study focuses on one of the most effective type of capacity markets named Capacity Subscription (CS market which is predicted to be widely used in the upcoming smart grids. Despite variant researches done about the mechanism and structure of capacity markets, their performances have been rarely tested in the presence of network constraints. Considering this deficiency, we tried to propose a new method to determine capacity prices in the network considering the transmission line flow limitations named Local capacity Prices (LP. This method is quite new and has not been tried before in any other similar researches. The philosophy of the proposed method is to determine capacity prices considering each consumer share of total peak demand. The first advantage of LP is that the consumers who benefit from the transmission facilities and are the responsible for transmission congestions, pay higher capacity prices than those whom their needed electricity is prepared locally. The second advantage of LP is that consumers connected to the same bus do not have to pay same capacity price due to their different shares of total peak demand. For more clarification, two other different methods named Branches Flow limit as a Global Limit (BFGL and Locational Capacity Prices (LCP are proposed and compared to the LP method in order to show LP method efficiency. The numerical results obtained from case studies show that the LP method follows more justice market procedure which results in more efficient capacity prices in comparison to BFGL and LCP methods.

Economics of access versus ownership the costs and benefits of access to scholarly articles via interlibrary loan and journal subscriptions

CERN Document Server

Kingma, Bruce

2013-01-01

The Economics of Access Versus Ownership offers library professionals a model economic analysis of providing access to journal articles through interlibrary loan as compared to library subscriptions to the journals. This model enables library directors to do an economic analysis of interlibrary loan and collection development in their own libraries and to then make cost-efficient decisions about the use of these services.This practical book's analysis and conclusions are based on 1994/95 academic year research conducted by the State University of New York libraries at Albany, Binghamton, Buffa

Modified Regression Correlation Coefficient for Poisson Regression Model

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Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Responsibility Towards The Customers Of Subscription-Based Software Solutions In The Context Of Using The Cloud Computing Technology

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Bogdan Ștefan Ionescu

2003-12-01

Full Text Available The continuously transformation of the contemporary society and IT environment circumscribed its informational has led to the emergence of the cloud computing technology that provides the access to infrastructure and subscription-based software services, as well. In the context of a growing number of service providers with of cloud software, the paper aims to identify the perception of some current or potential users of the cloud solution, selected from among students enrolled in the accounting (professional or research master programs with the profile organized by the Bucharest University of Economic Studies, in terms of their expectations for cloud services, as well as the extent to which the SaaS providers are responsible for the provided services.

Capacity subscription and its market design impact

International Nuclear Information System (INIS)

Doorman, Gerard; Solem, Gerd

2005-04-01

Capacity Subscription (CS) implies that consumers buy (subscribe to) a certain amount of capacity. Their demand is limited to this capacity when the total power system is short of capacity and the System Operator activates controllable Load Limiting Devices (LLDs). The objective is to maintain system security by avoiding involuntary load shedding. The report describes a market design with CS. As a case study, an analysis is made of the changes in the market design of the Nordic system that would be necessary to implement CS. First the present Nordic market design is described. Focus is on the various market participants, their roles within various time horizons and their interactions. So it is described how CS works, why it works and what is necessary to make it work. Subsequently the necessary changes in the Nordic market structure are described. The major changes are the installation of the LLDs, the establishment of the necessary infrastructure to control the LLDs and the rules governing their control and the establishment of a capacity market. The major rule is that the System Operator announces LLD activation when a shortage situation is expected. In the capacity market generators offer available capacity during system peak conditions, while consumers bid their need for capacity. Market participants are the same as on the spot market, while small consumers buy through retailer. Generators are obliged to offer the capacity sold on the capacity market on the spot market during LLD activation. Failure to do so results in a penalty payment. The report further discusses issues like the need for verification procedures, import and export, generation pooling, the handling of small consumers, reserves and a possible implementation path of CS. With respect to transmission constraints it is argued that market splitting can be a viable option. It is concluded that CS can be a possible solution to maintain generation adequacy, but there are some serious challenges. The

The Association of Serum 25-Hydroxyvitamin D3 and D2 with Depressive Symptoms in Childhood--A Prospective Cohort Study

Science.gov (United States)

Tolppanen, Anna-Maija; Sayers, Adrian; Fraser, William D.; Lewis, Glyn; Zammit, Stanley; Lawlor, Debbie A.

2012-01-01

Background: Depression in adolescence is common and early onset predicts worse outcome in adulthood. Studies in adults have suggested a link between higher total 25-hydroxyvitamin D [25(OH)D] concentrations and lower risk of depression. Objectives: To investigate (a) the association between serum 25(OH)D[subscript 2] and 25(OH)D[subscript 3]…

Dual Regression

OpenAIRE

Spady, Richard; Stouli, Sami

2012-01-01

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

Suscripción de revistas en línea en la Argentina, 2007 On line journals subscription in Argentina, 2007

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Susana Romanos de Tiratel

2011-12-01

Full Text Available El artículo presenta los resultados de una investigación orientada a describir aspectos del proceso de suscripción de revistas en línea en la Argentina. Entre abril y setiembre del año 2007 se aplicó una encuesta vía correo electrónico a bibliotecas especializadas y universitarias argentinas para indagar acerca de los actores involucrados en la identificación, evaluación y selección de títulos, en la negociación y firma de las licencias, las modalidades de suscripción y los tipos de recursos suscriptos por esas unidades de información. Se observó una alta participación de los bibliotecarios en todo el proceso que disminuía en la etapa de negociación y firma de los acuerdos de licencia donde los directores institucionales y los administradores tenían un rol más destacado.This paper presents the findings of an investigation aimed at describing aspects of the process of online journals subscription in Argentina. From April to September of 2007 a survey was conducted via electronic mail to special and academic libraries to inquire about Argentine actors involved in the identification, evaluation, and selection of titles, in the negotiation and signing of the licences, the subscription methods, and type of resources subscribed for those information units. There was a high participation of librarians in the process that decreased in the stage of negotiating and signing the licencing agreements where institutional managers and administrators had a more prominent role.

Shrinkage Degree in $L_{2}$ -Rescale Boosting for Regression.

Science.gov (United States)

Xu, Lin; Lin, Shaobo; Wang, Yao; Xu, Zongben

2017-08-01

L 2 -rescale boosting ( L 2 -RBoosting) is a variant of L 2 -Boosting, which can essentially improve the generalization performance of L 2 -Boosting. The key feature of L 2 -RBoosting lies in introducing a shrinkage degree to rescale the ensemble estimate in each iteration. Thus, the shrinkage degree determines the performance of L 2 -RBoosting. The aim of this paper is to develop a concrete analysis concerning how to determine the shrinkage degree in L 2 -RBoosting. We propose two feasible ways to select the shrinkage degree. The first one is to parameterize the shrinkage degree and the other one is to develop a data-driven approach. After rigorously analyzing the importance of the shrinkage degree in L 2 -RBoosting, we compare the pros and cons of the proposed methods. We find that although these approaches can reach the same learning rates, the structure of the final estimator of the parameterized approach is better, which sometimes yields a better generalization capability when the number of sample is finite. With this, we recommend to parameterize the shrinkage degree of L 2 -RBoosting. We also present an adaptive parameter-selection strategy for shrinkage degree and verify its feasibility through both theoretical analysis and numerical verification. The obtained results enhance the understanding of L 2 -RBoosting and give guidance on how to use it for regression tasks.

Are CO2 taxes regressive? Evidence from the Danish experience

International Nuclear Information System (INIS)

Wier, Mette; Birr-Pedersen, Katja; Jacobsen, Henrik Klinge; Klok, Jacob

2005-01-01

Denmark today carries one of the heaviest environmental tax burdens in the world, bringing in around 10% of public revenues. While evaluations have shown that the Danish CO 2 and other environmental taxes work as an effective measure to reduce emissions, a considerable barrier to increased use of these instruments today seems to be a widespread perception of their socially adverse effects. In this article, it is demonstrated that CO 2 taxes imposed on energy consumption in households, as well as in industry, do in fact tend to be regressive, and therefore have undesirable distributional effects. This holds especially for taxes imposed directly on households. To analyze this, we apply national consumer survey statistics in combination with input-output tables

Making Small-Scale Classroom Greenhouse Gas Flux Calculations Using a Handmade Gas Capture Hood

Science.gov (United States)

Schouten, Peter W.; Sharma, Ashok; Burn, Stewart; Goodman, Nigel; Parisi, Alfio; Downs, Nathan; Lemckert, Charles

2013-01-01

The emissions of various types of greenhouse gases (GHGs) from natural and industrial sources are undergoing a great deal of scrutiny around the world. The three main GHGs that are of most concern are carbon dioxide (CO[subscript 2]), nitrous oxide (N[subscript 2]O) and methane (CH[subscript 4]). CO[subscript 2], N[subscript 2]O and CH[subscript…

Inference for the Bivariate and Multivariate Hidden Truncated Pareto(type II) and Pareto(type IV) Distribution and Some Measures of Divergence Related to Incompatibility of Probability Distribution

Science.gov (United States)

Ghosh, Indranil

2011-01-01

Consider a discrete bivariate random variable (X, Y) with possible values x[subscript 1], x[subscript 2],..., x[subscript I] for X and y[subscript 1], y[subscript 2],..., y[subscript J] for Y. Further suppose that the corresponding families of conditional distributions, for X given values of Y and of Y for given values of X are available. We…

Fungible weights in logistic regression.

Science.gov (United States)

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Linear regression in astronomy. II

Science.gov (United States)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Estimativa da composição química corporal de tourinhos Santa Gertrudes a partir da composição química e física das 9-10-11ª costelas

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Henrique Wignez

2003-01-01

Full Text Available Foram obtidas equações de regressão linear simples para estimar a composição química corporal de bovinos Santa Gertrudes, a partir da composição química e física do corte das 9-10-11ª costelas. Quinze tourinhos, entre nove a 15 meses de idade e de 220 a 505 kg de peso, foram mantidos confinados. Os animais foram abatidos após jejum completo de 18 horas, sendo que seis deles foram abatidos após adaptação. A composição química em água, proteína, extrato etéreo e minerais foi determinada no corte das costelas e em amostras obtidas após moagem completa e homogeneização de todos os tecidos corporais, divididos em: sangue, couro, cabeça + patas, vísceras e carcaça. A composição física do corte das costelas foi obtida por separação manual do músculo, gordura e ossos. O peso do corpo vazio foi altamente correlacionado ao peso da carcaça quente (rsuperscript two = 0,99. As porcentagens de água e extrato etéreo das 9-10-11ª costelas mostraram-se altamente correlacionadas com a composição química do corpo vazio, o que não ocorreu para as porcentagens de proteína e minerais. Esses teores foram calculados pela composição do corpo vazio desengordurado. A composição física do corte das costelas foi eficiente para estimar as porcentagens de água, extrato etéreo e minerais do corpo vazio, utilizando-se a porcentagem de gordura separável das costelas, mas não para estimar o teor de proteína. A composição física do corte das costelas demonstrou ser uma técnica eficiente, mas a composição química apresentou maiores coeficientes de determinação e menores erros da estimativa. Como a porcentagem de água no corpo vazio e no corte das costelas (rsuperscript two = 0,95, e as porcentagens de água e de extrato etéreo no corpo vazio foram altamente correlacionadas (rsuperscript two = 0,94, a porcentagem de água no corte das 9-10-11ª costelas poderia ser a única variável para estimativa da composição qu

Regression: A Bibliography.

Science.gov (United States)

Pedrini, D. T.; Pedrini, Bonnie C.

Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression

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Maokuan Zheng

2017-01-01

Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution

International Nuclear Information System (INIS)

Shaby, Benjamin A.; Field, Christopher B.

2006-01-01

In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms

Regression Modeling of EDM Process for AISI D2 Tool Steel with RSM

Directory of Open Access Journals (Sweden)

Shakir M. Mousa

2018-01-01

Full Text Available In this paper, Response Surface Method (RSM is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T. [Gr, Cu and CuW], pulse duration of current (Ip, pulse duration on time (Ton, and pulse duration off time (Toff on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra. Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of Variance (ANOVA at the 5 % confidence interval. The outcomes discover that Surface Roughness (Ra is much more impacted by E.T., Ton, Toff, Ip and little of their interactions action or influence. To predict the average Surface Roughness (Ra, a mathematical regression model was developed. Furthermore, for saving in time, the created model could be utilized for the choice of the high levels in the EDM procedure. The model adequacy was extremely agreeable as the constant Coefficient of Determination (R2 is observed to be 99.72% and adjusted R2-measurement (R2adj 99.60%.

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

Variation of Parameters in Differential Equations (A Variation in Making Sense of Variation of Parameters)

Science.gov (United States)

Quinn, Terry; Rai, Sanjay

2012-01-01

The method of variation of parameters can be found in most undergraduate textbooks on differential equations. The method leads to solutions of the non-homogeneous equation of the form y = u[subscript 1]y[subscript 1] + u[subscript 2]y[subscript 2], a sum of function products using solutions to the homogeneous equation y[subscript 1] and…

Orbital Exponent Optimization in Elementary VB Calculations of the Chemical Bond in the Ground State of Simple Molecular Systems

Science.gov (United States)

Magnasco, Valerio

2008-01-01

Orbital exponent optimization in the elementary ab-initio VB calculation of the ground states of H[subscript 2][superscript +], H[subscript 2], He[subscript 2][superscript +], He[subscript 2] gives a fair description of the exchange-overlap component of the interatomic interaction that is important in the bond region. Correct bond lengths and…

Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country

NARCIS (Netherlands)

Hoek, G.; Eeftens, M.; Beelen, R.; Fischer, P.; Brunekreef, B.; Boersma, K.F.; Veefkind, P.

2015-01-01

Land use regression (LUR) modelling has increasingly been applied to model fine scale spatial variation of outdoor air pollutants including nitrogen dioxide (NO2). Satellite observations of tropospheric NO2 improved LUR model in very large study areas, including Canada, United States and Australia.

Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country

NARCIS (Netherlands)

Hoek, Gerard; Eeftens, Marloes; Beelen, Rob; Fischer, Paul; Brunekreef, Bert; Boersma, K. Folkert; Veefkind, Pepijn

Land use regression (LUR) modelling has increasingly been applied to model fine scale spatial variation of outdoor air pollutants including nitrogen dioxide (NO2). Satellite observations of tropospheric NO2 improved LUR model in very large study areas, including Canada, United States and Australia.

The Classical Version of Stokes' Theorem Revisited

Science.gov (United States)

Markvorsen, Steen

2008-01-01

Using only fairly simple and elementary considerations--essentially from first year undergraduate mathematics--we show how the classical Stokes' theorem for any given surface and vector field in R[superscript 3] follows from an application of Gauss' divergence theorem to a suitable modification of the vector field in a tubular shell around the…

Subscribers and newspaper subscriptions in Spain at the beginning of the XX century. Notes from an asturian perspective

Directory of Open Access Journals (Sweden)

Víctor Rodríguez Infiesta

2008-12-01

Full Text Available In the first decades of the XX century the possibility of a particular newspaper having a high number of subscribers was, from the point of view of the newspaper publishers, the best guarantee of its stability. However it was not all advantages in a system which gave rise to numerous complaints and created a peculiar relationship with the subscriber. By various means —delivery men, the post and public establishments, principally— the subscription service continually generated situations illustrative of the level of development of the Spanish press in years in which progress accelerates and deficiencies and limitations also become apparent. Limitations which, particularly in an outlying region with mostly inadequate means of communication such as Asturias, could become one of the main factors delaying the establishment of a large press with mass-readership.

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

Energy Diagram for the Catalytic Decomposition of Hydrogen Peroxide

Science.gov (United States)

Tatsuoka, Tomoyuki; Koga, Nobuyoshi

2013-01-01

Drawing a schematic energy diagram for the decomposition of H[subscript 2]O[subscript 2] catalyzed by MnO[subscript 2] through a simple thermometric measurement outlined in this study is intended to integrate students' understanding of thermochemistry and kinetics of chemical reactions. The reaction enthalpy, delta[subscript r]H, is…

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data

International Nuclear Information System (INIS)

Lefevre, Nathalie; Watson, Andrew J.; Watson, Adam R.

2005-01-01

Using about 138,000 measurements of surface pCO 2 in the Atlantic subpolar gyre (50-70 deg N, 60-10 deg W) during 1995-1997, we compare two methods of interpolation in space and time: a monthly distribution of surface pCO 2 constructed using multiple linear regressions on position and temperature, and a self-organizing neural network approach. Both methods confirm characteristics of the region found in previous work, i.e. the subpolar gyre is a sink for atmospheric CO 2 throughout the year, and exhibits a strong seasonal variability with the highest undersaturations occurring in spring and summer due to biological activity. As an annual average the surface pCO 2 is higher than estimates based on available syntheses of surface pCO 2 . This supports earlier suggestions that the sink of CO 2 in the Atlantic subpolar gyre has decreased over the last decade instead of increasing as previously assumed. The neural network is able to capture a more complex distribution than can be well represented by linear regressions, but both techniques agree relatively well on the average values of pCO 2 and derived fluxes. However, when both techniques are used with a subset of the data, the neural network predicts the remaining data to a much better accuracy than the regressions, with a residual standard deviation ranging from 3 to 11 μatm. The subpolar gyre is a net sink of CO 2 of 0.13 Gt-C/yr using the multiple linear regressions and 0.15 Gt-C/yr using the neural network, on average between 1995 and 1997. Both calculations were made with the NCEP monthly wind speeds converted to 10 m height and averaged between 1995 and 1997, and using the gas exchange coefficient of Wanninkhof

Reduced Rank Regression

DEFF Research Database (Denmark)

Johansen, Søren

2008-01-01

The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

Analysis of Static and Dynamic E-Reference Content at a Multi-Campus University Shows, that Updated Content is Associated with Greater Annual Usage

Directory of Open Access Journals (Sweden)

Laura Costello

2016-03-01

Full Text Available Objective – To discover whether there is a difference in use over time between dynamically updated and changing subscription e-reference titles and collections, and static purchased e-reference titles and collections. Design – Case study. Setting – A multi-campus Canadian university with 9,200 students enrolled in both graduate and undergraduate programs. Subjects – E-reference book packages and individual e-reference titles. Methods – The author compared data from individual e-reference books and packages. First, individual subscription e-reference books that periodically added updated content were compared to individually purchased e-reference books that remained static after purchase. The author then compared two e-reference book packages that provided new and updated content to two static e-reference book packages. The author compared data from patron usage to new content added over time using regression analysis. Main Results – As the library acquired e-reference titles, dynamic title subscriptions added to the collection were associated with 2,246 to 4,635 views per subscription while static title additions were associated with 8 to 123 views per purchase. The author also found that there was a strong linear relationship between views and dynamic titles added to the collection (R2=0.79 and a very weak linear relationship (R2=0.18 with views when static titles are added to the collection. Regression analysis of dynamic e-reference collections revealed that the number of titles added to each collection was strongly associated with views of the material (R2=0.99, while static e-reference collections were less strongly linked (R2=0.43. Conclusion – Dynamic e-reference titles and collections experienced increases in usage each year while static titles and collections experienced decreases in usage. This indicates that collections and titles that offer new content to users each year will continue to see growth in usage while static

BOX-COX REGRESSION METHOD IN TIME SCALING

Directory of Open Access Journals (Sweden)

ATİLLA GÖKTAŞ

2013-06-01

Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

Regression Phalanxes

OpenAIRE

Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

2017-01-01

Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

Science.gov (United States)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Prediction of hourly PM2.5 using a space-time support vector regression model

Science.gov (United States)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

On weighted and locally polynomial directional quantile regression

Czech Academy of Sciences Publication Activity Database

Boček, Pavel; Šiman, Miroslav

2017-01-01

Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.434, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf

Distributed Monitoring of the R2 Statistic for Linear Regression

Data.gov (United States)

National Aeronautics and Space Administration — The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and...

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Does the high–tech industry consistently reduce CO{sub 2} emissions? Results from nonparametric additive regression model

Energy Technology Data Exchange (ETDEWEB)

Xu, Bin [School of Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013 (China); Lin, Boqiang, E-mail: bqlin@xmu.edu.cn [Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Xiamen, Fujian 361005 (China)

2017-03-15

China is currently the world's largest carbon dioxide (CO{sub 2}) emitter. Moreover, total energy consumption and CO{sub 2} emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO{sub 2} emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO{sub 2} emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO{sub 2} emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO{sub 2} emissions in the three regions should be given adequate attention in developing effective abatement policies. - Highlights: • The nonlinear effect of the high–tech industry on CO{sub 2} emissions was investigated. • The high–tech industry yields an inverted “U–shaped” effect in the eastern region. • The high–tech industry has a positive “U–shaped” nonlinear effect in other regions. • The linear impact

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

NARCIS (Netherlands)

de Hoogh, Kees; Gulliver, John; Donkelaar, Aaron van; Martin, Randall V; Marshall, Julian D; Bechle, Matthew J; Cesaroni, Giulia; Pradas, Marta Cirach; Dedele, Audrius; Eeftens, Marloes|info:eu-repo/dai/nl/315028300; Forsberg, Bertil; Galassi, Claudia; Heinrich, Joachim; Hoffmann, Barbara; Jacquemin, Bénédicte; Katsouyanni, Klea; Korek, Michal; Künzli, Nino; Lindley, Sarah J; Lepeule, Johanna; Meleux, Frederik; de Nazelle, Audrey; Nieuwenhuijsen, Mark; Nystad, Wenche; Raaschou-Nielsen, Ole; Peters, Annette; Peuch, Vincent-Henri; Rouil, Laurence; Udvardy, Orsolya; Slama, Rémy; Stempfelet, Morgane; Stephanou, Euripides G; Tsai, Ming Y; Yli-Tuomi, Tarja; Weinmayr, Gudrun; Brunekreef, Bert|info:eu-repo/dai/nl/067548180; Vienneau, Danielle; Hoek, Gerard|info:eu-repo/dai/nl/069553475

2016-01-01

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models,

[From clinical judgment to linear regression model.

Science.gov (United States)

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Association between number of cell phone contracts and brain tumor incidence in nineteen U.S. States.

Science.gov (United States)

Lehrer, Steven; Green, Sheryl; Stock, Richard G

2011-02-01

Some concern has arisen about adverse health effects of cell phones, especially the possibility that the low power microwave-frequency signal transmitted by the antennas on handsets might cause brain tumors or accelerate the growth of subclinical tumors. We analyzed data from the Statistical Report: Primary Brain Tumors in the United States, 2000-2004 and 2007 cell phone subscription data from the Governing State and Local Sourcebook. There was a significant correlation between number of cell phone subscriptions and brain tumors in nineteen US states (r = 0.950, P cell phone subscriptions and brain tumors could be due solely to the fact that some states, such as New York, have much larger populations than other states, such as North Dakota, multiple linear regression was performed with number of brain tumors as the dependent variable, cell phone subscriptions, population, mean family income and mean age as independent variables. The effect of cell phone subscriptions was significant (P = 0.017), and independent of the effect of mean family income (P = 0.894), population (P = 0.003) and age (0.499). The very linear relationship between cell phone usage and brain tumor incidence is disturbing and certainly needs further epidemiological evaluation. In the meantime, it would be prudent to limit exposure to all sources of electro-magnetic radiation.

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

Science.gov (United States)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

Science.gov (United States)

Bulcock, J. W.; And Others

Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

Regression: The Apple Does Not Fall Far From the Tree.

Science.gov (United States)

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Multivariate Regression of Liver on Intestine of Mice: A ...

African Journals Online (AJOL)

FIRST LADY

pairs recovered. Linear, semi-logarithmic and logarithmic-logarithmic (log- log) regressions were performed. He chose the log-log curves because its variance was more uniform. The statistical comparison of .... E(U1| U2 = u2) is the regression function of U1 on U2, and Var (U1|U2 = u2) is the conditional covariance matrix.

Regression analysis with categorized regression calibrated exposure: some interesting findings

Directory of Open Access Journals (Sweden)

Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

Fermat and the Solution of X[cubed]-2=Y[squared

Science.gov (United States)

Leyendekkers, J. V.; Shannon, A. G.

2002-01-01

Using the modular ring Zeta[subscript 4], simple algebra is used to study diophantine equations of the form (x[cubed]-a=y[squared]). Fermat challenged his contemporaries to solve this equation when a = 2. They were unable to do so, although Fermat had devised a rather complicated proof himself. (Contains 2 tables.)

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

Directory of Open Access Journals (Sweden)

Jingyi Zhang

2018-06-01

Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

Science.gov (United States)

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Simple and multiple linear regression: sample size considerations.

Science.gov (United States)

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

HMGB1 mediates endogenous TLR2 activation and brain tumor regression.

Directory of Open Access Journals (Sweden)

James F Curtin

2009-01-01

Full Text Available Glioblastoma multiforme (GBM is the most aggressive primary brain tumor that carries a 5-y survival rate of 5%. Attempts at eliciting a clinically relevant anti-GBM immune response in brain tumor patients have met with limited success, which is due to brain immune privilege, tumor immune evasion, and a paucity of dendritic cells (DCs within the central nervous system. Herein we uncovered a novel pathway for the activation of an effective anti-GBM immune response mediated by high-mobility-group box 1 (HMGB1, an alarmin protein released from dying tumor cells, which acts as an endogenous ligand for Toll-like receptor 2 (TLR2 signaling on bone marrow-derived GBM-infiltrating DCs.Using a combined immunotherapy/conditional cytotoxic approach that utilizes adenoviral vectors (Ad expressing Fms-like tyrosine kinase 3 ligand (Flt3L and thymidine kinase (TK delivered into the tumor mass, we demonstrated that CD4(+ and CD8(+ T cells were required for tumor regression and immunological memory. Increased numbers of bone marrow-derived, tumor-infiltrating myeloid DCs (mDCs were observed in response to the therapy. Infiltration of mDCs into the GBM, clonal expansion of antitumor T cells, and induction of an effective anti-GBM immune response were TLR2 dependent. We then proceeded to identify the endogenous ligand responsible for TLR2 signaling on tumor-infiltrating mDCs. We demonstrated that HMGB1 was released from dying tumor cells, in response to Ad-TK (+ gancyclovir [GCV] treatment. Increased levels of HMGB1 were also detected in the serum of tumor-bearing Ad-Flt3L/Ad-TK (+GCV-treated mice. Specific activation of TLR2 signaling was induced by supernatants from Ad-TK (+GCV-treated GBM cells; this activation was blocked by glycyrrhizin (a specific HMGB1 inhibitor or with antibodies to HMGB1. HMGB1 was also released from melanoma, small cell lung carcinoma, and glioma cells treated with radiation or temozolomide. Administration of either glycyrrhizin or anti

On directional multiple-output quantile regression

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2011-01-01

Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf

Factors affecting CO_2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model

International Nuclear Information System (INIS)

Xu, Bin; Lin, Boqiang

2017-01-01

China is currently the world's largest emitter of carbon dioxide. Considered as a large agricultural country, carbon emission in China’s agriculture sector keeps on growing rapidly. It is, therefore, of great importance to investigate the driving forces of carbon dioxide emissions in this sector. The traditional regression estimation can only get “average” and “global” parameter estimates; it excludes the “local” parameter estimates which vary across space in some spatial systems. Geographically weighted regression embeds the latitude and longitude of the sample data into the regression parameters, and uses the local weighted least squares method to estimate the parameters point–by–point. To reveal the nonstationary spatial effects of driving forces, geographically weighted regression model is employed in this paper. The results show that economic growth is positively correlated with emissions, with the impact in the western region being less than that in the central and eastern regions. Urbanization is positively related to emissions but produces opposite effects pattern. Energy intensity is also correlated with emissions, with a decreasing trend from the eastern region to the central and western regions. Therefore, policymakers should take full account of the spatial nonstationarity of driving forces in designing emission reduction policies. - Highlights: • We explore the driving forces of CO_2 emissions in the agriculture sector. • Urbanization is positively related to emissions but produces opposite effect pattern. • The effect of energy intensity declines from the eastern region to western region.

Simple Photovoltaic Cells for Exploring Solar Energy Concepts

Science.gov (United States)

Appleyard, S. J.

2006-01-01

Low-efficiency solar cells for educational purposes can be simply made in school or home environments using wet-chemistry techniques and readily available chemicals of generally low toxicity. Instructions are given for making solar cells based on the heterojunctions Cu/Cu[subscript 2]O, Cu[subscript 2]O/ZnO and Cu[subscript 2]S/ZnO, together with…

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

Applied logistic regression

CERN Document Server

Hosmer, David W; Sturdivant, Rodney X

2013-01-01

 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

National health insurance subscription and maternal healthcare utilisation across mothers' wealth status in Ghana.

Science.gov (United States)

Ameyaw, Edward Kwabena; Kofinti, Raymond Elikplim; Appiah, Francis

2017-12-01

This study is against the backdrop that despite the forty-nine percent decline in Maternal Mortality Rate in Ghana, the situation still remains high averaging 319 per 100,000 live births between 2011 and 2015. To examine the relationship between National Health Insurance and maternal healthcare utilisation across three main wealth quintiles (Poor, Middle and Rich). The study employed data from the 2014 Ghana Demographic and Health Survey. Both descriptive analysis and binary logistic regression were conducted. Descriptively, rich women had high antenatal attendance and health facility deliveries represented by 96.5% and 95.6% respectively. However, the binary logistic regression results revealed that poor women owning NHIS are 7% (CI = 1.76-2.87) more likely to make at least four antenatal care visits compared to women in the middle wealth quintile (5%, CI = 2.12-4.76) and rich women (2%, CI = 1.14-4.14). Similarly, poor women who owned the NHIS are 14% (CI = 1.42-2.13) likely to deliver in health facility than women in the middle and rich wealth quintile. The study has vindicated the claim that NHIS Scheme is pro-poor in Ghana. The Ministry of Health should target women in the rural area to be enrolled on the NHIS to improve maternal healthcare utilisation since poverty is principally a rural phenomenon in Ghana.

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

Science.gov (United States)

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

A + (B[subscript 1]) Professor--student Rapport + (B[subscript 2]) Humor + (B[subscript 3]) Student Engagement = (Y) Student Ratings of Instructors

Science.gov (United States)

Richmond, Aaron S.; Berglund, Majken B.; Epelbaum, Vadim B.; Klein, Eric M.

2015-01-01

Teaching effectiveness is often evaluated through student ratings of instruction (SRI). Research suggests that there are many potential factors that can predict student's perceptions of teaching effectiveness such as professor-student rapport, student engagement, and perceived humor of the instructor. Therefore, we sought to assess whether…

Vector regression introduced

Directory of Open Access Journals (Sweden)

Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Applied linear regression

CERN Document Server

Weisberg, Sanford

2013-01-01

Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

Barriers and facilitators for the implementation of an online clinical health community in addition to usual fertility care: a cross-sectional study.

Science.gov (United States)

Aarts, Johanna W M; Faber, Marjan J; den Boogert, Anne G; Cohlen, Ben J; van der Linden, Paul J Q; Kremer, Jan A M; Nelen, Willianne L D M

2013-08-30

Online health communities are becoming more popular in health care. Patients and professionals can communicate with one another online, patients can find peer support, and professionals can use it as an additional information channel to their patients. However, the implementation of online health communities into daily practice is challenging. These challenges relate to the fact that patients need to be activated to (1) become a member (ie, subscription) and (2) participate actively within the community before any effect can be expected. Therefore, we aimed at answering 2 research questions: (1) what factors are associated with subscription to an online health community, and (2) which are associated with becoming an active participant within an online health community. To identify barriers and facilitators as perceived by patients for the implementation of an online health community. We performed a cross-sectional study. Three Dutch fertility clinics (2 IVF-licensed) offered their patients a secure online clinical health community through which clinicians can provide online information and patients can ask questions to the medical team or share experiences and find support from peers. We randomly selected and invited 278 men and women suffering from infertility and attending 1 of the participating clinics. Participants filled out a questionnaire about their background characteristics and current use of the online community. Possible barriers and facilitators were divided into 2 parts: (1) those for subscription to the community, and (2) those for active participation in the community. We performed 2 multivariate logistic regression analyses to calculate determinants for both subscription and active participation. Subscription appeared to be associated with patients' background characteristics (eg, gender, treatment phase), intervention-related facilitators (odds ratio [OR] 2.45, 95% CI 1.14-5.27), and patient-related barriers (OR 0.20, 95% CI 0.08-0.54), such as

The number of subjects per variable required in linear regression analyses.

Science.gov (United States)

Austin, Peter C; Steyerberg, Ewout W

2015-06-01

To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Dual Studies on a Hydrogen-Deuterium Exchange of Resorcinol and the Subsequent Kinetic Isotope Effect

Science.gov (United States)

Giles, Richard; Kim, Iris; Chao, Weyjuin Eric; Moore, Jennifer; Jung, Kyung Woon

2014-01-01

An efficient laboratory experiment has been developed for undergraduate students to conduct hydrogen-deuterium (H-D) exchange of resorcinol by electrophilic aromatic substitution using D[subscript 2]O and a catalytic amount of H[subscript 2]SO[subscript 4]. The resulting labeled product is characterized by [superscript 1]H NMR. Students also…

How Should We Assess the Fit of Rasch-Type Models? Approximating the Power of Goodness-of-Fit Statistics in Categorical Data Analysis

Science.gov (United States)

Maydeu-Olivares, Alberto; Montano, Rosa

2013-01-01

We investigate the performance of three statistics, R [subscript 1], R [subscript 2] (Glas in "Psychometrika" 53:525-546, 1988), and M [subscript 2] (Maydeu-Olivares & Joe in "J. Am. Stat. Assoc." 100:1009-1020, 2005, "Psychometrika" 71:713-732, 2006) to assess the overall fit of a one-parameter logistic model…

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

Introduction to regression graphics

CERN Document Server

Cook, R Dennis

2009-01-01

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

Microscale Electrolysis Using Coin-Type Lithium Batteries and Filter

Science.gov (United States)

Kamata, Masahiro; Yajima, Seiko

2013-01-01

An educational experiment illustrates the electrolysis of water and copper chloride to middle school science students. The electrolysis cell is composed of filter paper soaked with Na[subscript 2]SO[subscript 4] or CuCl[subscript 2] aqueous solution sandwiched, along with a sheet of platinum foil, between two coin-type lithium batteries. When the…

Hydrothermal Synthesis and Characterization of a Metal-Organic Framework by Thermogravimetric Analysis, Powder X-Ray Diffraction, and Infrared Spectroscopy: An Integrative Inorganic Chemistry Experiment

Science.gov (United States)

Crane, Johanna L.; Anderson, Kelly E.; Conway, Samantha G.

2015-01-01

This advanced undergraduate laboratory experiment involves the synthesis and characterization of a metal-organic framework with microporous channels that are held intact via hydrogen bonding of the coordinated water molecules. The hydrothermal synthesis of Co[subscript 3](BTC)[subscript 2]·12H[subscript 2]O (BTC = 1,3,5-benzene tricarboxylic acid)…

Testing the equality of nonparametric regression curves based on ...

African Journals Online (AJOL)

Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...

Chemokine receptors CXCR2 and CX3CR1 differentially regulate functional responses of bone-marrow endothelial progenitors during atherosclerotic plaque regression

Science.gov (United States)

Herlea-Pana, Oana; Yao, Longbiao; Heuser-Baker, Janet; Wang, Qiongxin; Wang, Qilong; Georgescu, Constantin; Zou, Ming-Hui; Barlic-Dicen, Jana

2015-01-01

Aims Atherosclerosis manifests itself as arterial plaques, which lead to heart attacks or stroke. Treatments supporting plaque regression are therefore aggressively pursued. Studies conducted in models in which hypercholesterolaemia is reversible, such as the Reversa mouse model we have employed in the current studies, will be instrumental for the development of such interventions. Using this model, we have shown that advanced atherosclerosis regression occurs when lipid lowering is used in combination with bone-marrow endothelial progenitor cell (EPC) treatment. However, it remains unclear how EPCs home to regressing plaques and how they augment atherosclerosis reversal. Here we identify molecules that support functional responses of EPCs during plaque resolution. Methods and results Chemokines CXCL1 and CX3CL1 were detected in the vascular wall of atheroregressing Reversa mice, and their cognate receptors CXCR2 and CX3CR1 were observed on adoptively transferred EPCs in circulation. We tested whether CXCL1–CXCR2 and CX3CL1–CX3CR1 axes regulate functional responses of EPCs during plaque reversal. We show that pharmacological inhibition of CXCR2 or CX3CR1, or genetic inactivation of these two chemokine receptors interfered with EPC-mediated advanced atherosclerosis regression. We also demonstrate that CXCR2 directs EPCs to regressing plaques while CX3CR1 controls a paracrine function(s) of these cells. Conclusion CXCR2 and CX3CR1 differentially regulate EPC functional responses during atheroregression. Our study improves understanding of how chemokines and chemokine receptors regulate plaque resolution, which could determine the effectiveness of interventions reducing complications of atherosclerosis. PMID:25765938

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Using support vector regression to predict PM10 and PM2.5

International Nuclear Information System (INIS)

Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

2014-01-01

Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying

Land-use regression panel models of NO2 concentrations in Seoul, Korea

Science.gov (United States)

Kim, Youngkook; Guldmann, Jean-Michel

2015-04-01

Transportation and land-use activities are major air pollution contributors. Since their shares of emissions vary across space and time, so do air pollution concentrations. Despite these variations, panel data have rarely been used in land-use regression (LUR) modeling of air pollution. In addition, the complex interactions between traffic flows, land uses, and meteorological variables, have not been satisfactorily investigated in LUR models. The purpose of this research is to develop and estimate nitrogen dioxide (NO2) panel models based on the LUR framework with data for Seoul, Korea, accounting for the impacts of these variables, and their interactions with spatial and temporal dummy variables. The panel data vary over several scales: daily (24 h), seasonally (4), and spatially (34 intra-urban measurement locations). To enhance model explanatory power, wind direction and distance decay effects are accounted for. The results show that vehicle-kilometers-traveled (VKT) and solar radiation have statistically strong positive and negative impacts on NO2 concentrations across the four seasonal models. In addition, there are significant interactions with the dummy variables, pointing to VKT and solar radiation effects on NO2 concentrations that vary with time and intra-urban location. The results also show that residential, commercial, and industrial land uses, and wind speed, temperature, and humidity, all impact NO2 concentrations. The R2 vary between 0.95 and 0.98.

Independent contrasts and PGLS regression estimators are equivalent.

Science.gov (United States)

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Analyzing hospitalization data: potential limitations of Poisson regression.

Science.gov (United States)

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Linear regression in astronomy. I

Science.gov (United States)

Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

1990-01-01

Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

Logic regression and its extensions.

Science.gov (United States)

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Spontaneous regression of retinopathy of prematurity:incidence and predictive factors

Directory of Open Access Journals (Sweden)

Rui-Hong Ju

2013-08-01

Full Text Available AIM:To evaluate the incidence of spontaneous regression of changes in the retina and vitreous in active stage of retinopathy of prematurity(ROP and identify the possible relative factors during the regression.METHODS: This was a retrospective, hospital-based study. The study consisted of 39 premature infants with mild ROP showed spontaneous regression (Group A and 17 with severe ROP who had been treated before naturally involuting (Group B from August 2008 through May 2011. Data on gender, single or multiple pregnancy, gestational age, birth weight, weight gain from birth to the sixth week of life, use of oxygen in mechanical ventilation, total duration of oxygen inhalation, surfactant given or not, need for and times of blood transfusion, 1,5,10-min Apgar score, presence of bacterial or fungal or combined infection, hyaline membrane disease (HMD, patent ductus arteriosus (PDA, duration of stay in the neonatal intensive care unit (NICU and duration of ROP were recorded.RESULTS: The incidence of spontaneous regression of ROP with stage 1 was 86.7%, and with stage 2, stage 3 was 57.1%, 5.9%, respectively. With changes in zone Ⅲ regression was detected 100%, in zoneⅡ 46.2% and in zoneⅠ 0%. The mean duration of ROP in spontaneous regression group was 5.65±3.14 weeks, lower than that of the treated ROP group (7.34±4.33 weeks, but this difference was not statistically significant (P=0.201. GA, 1min Apgar score, 5min Apgar score, duration of NICU stay, postnatal age of initial screening and oxygen therapy longer than 10 days were significant predictive factors for the spontaneous regression of ROP (P＜0.05. Retinal hemorrhage was the only independent predictive factor the spontaneous regression of ROP (OR 0.030, 95%CI 0.001-0.775, P=0.035.CONCLUSION:This study showed most stage 1 and 2 ROP and changes in zone Ⅲ can spontaneously regression in the end. Retinal hemorrhage is weakly inversely associated with the spontaneous regression.

Tumor regression patterns in retinoblastoma

International Nuclear Information System (INIS)

Zafar, S.N.; Siddique, S.N.; Zaheer, N.

2016-01-01

To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

Combining Alphas via Bounded Regression

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Zura Kakushadze

2015-11-01

Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

riskRegression

DEFF Research Database (Denmark)

Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

2017-01-01

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....

Journal of Social Development in Africa - Vol 15, No 2 (2000)

African Journals Online (AJOL)

Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Table of Contents. Articles. Child labour and education: a study from south-eastern Zimbabwe. M.F.C. Bourdillon. http://dx.doi.org/10.4314/jsda.v15i2.23857 · Poverty alleviation with economic growth strategy: Prospects and challenges in contemporary ...

Changes in persistence, spurious regressions and the Fisher hypothesis

DEFF Research Database (Denmark)

Kruse, Robinson; Ventosa-Santaulària, Daniel; Noriega, Antonio E.

Declining inflation persistence has been documented in numerous studies. When such series are analyzed in a regression framework in conjunction with other persistent time series, spurious regressions are likely to occur. We propose to use the coefficient of determination R2 as a test statistic to...

Regression in autistic spectrum disorders.

Science.gov (United States)

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

The Utility and Comparative Incremental Validity of the MMPI-2 and Trauma Symptom Inventory Validity Scales in the Detection of Feigned PTSD

Science.gov (United States)

Efendov, Adele A.; Sellbom, Martin; Bagby, R. Michael

2008-01-01

The authors examined the comparative predictive capacity of the Trauma Symptom Inventory (TSI) Atypical Response Scale (ATR) and the standard set of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) fake-bad validity scales (i.e., F, F[subscript B[prime

Understanding logistic regression analysis

OpenAIRE

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

Alternative regression models to assess increase in childhood BMI.

Science.gov (United States)

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-09-08

Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

Statistical Considerations in Choosing a Test Reliability Coefficient. ACT Research Report Series, 2012 (10)

Science.gov (United States)

Woodruff, David; Wu, Yi-Fang

2012-01-01

The purpose of this paper is to illustrate alpha's robustness and usefulness, using actual and simulated educational test data. The sampling properties of alpha are compared with the sampling properties of several other reliability coefficients: Guttman's lambda[subscript 2], lambda[subscript 4], and lambda[subscript 6]; test-retest reliability;…

[60]Fullerene Displacement from (Dihapto-Buckminster-Fullerene) Pentacarbonyl Tungsten(0): An Experiment for the Inorganic Chemistry Laboratory, Part II

Science.gov (United States)

Cortes-Figueroa, Jose E.; Moore-Russo, Deborah A.

2006-01-01

The kinetics experiments on the ligand-C[subscript 60] exchange reactions on (dihapto-[60]fullerene) pentacarbonyl tungsten(0), ([eta][superscript 2]-C[subscript 60])W(CO)[subscript 5], form an educational activity for the inorganic chemistry laboratory that promotes graphical thinking as well as the understanding of kinetics, mechanisms, and the…

A Matlab program for stepwise regression

Directory of Open Access Journals (Sweden)

Yanhong Qi

2016-03-01

Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.

Oxygen at Nanomolar Levels Reversibly Suppresses Process Rates and Gene Expression in Anammox and Denitrification in the Oxygen Minimum Zone off Northern Chile

OpenAIRE

Dalsgaard, Tage; Stewart, Frank J.; Thamdrup, Bo; De Brabandere, Loreto; Revsbech, Niels Peter; Ulloa, Osvaldo; Canfield, Don E.; DeLong, Edward

2014-01-01

A major percentage (20 to 40%) of global marine fixed-nitrogen loss occurs in oxygen minimum zones (OMZs). Concentrations of O[subscript 2] and the sensitivity of the anaerobic N[subscript 2]-producing processes of anammox and denitrification determine where this loss occurs. We studied experimentally how O[subscript 2] at nanomolar levels affects anammox and denitrification rates and the transcription of nitrogen cycle genes in the anoxic OMZ off Chile. Rates of anammox and denitrification w...

Mechanical Energy Changes in Perfectly Inelastic Collisions

Science.gov (United States)

Mungan, Carl E.

2013-01-01

Suppose a block of mass "m"[subscript 1] traveling at speed "v"[subscript 1] makes a one-dimensional perfectly inelastic collision with another block of mass "m"[subscript 2]. What else does one need to know to calculate the fraction of the mechanical energy that is dissipated in the collision? (Contains 1 figure.)

Quantile regression theory and applications

CERN Document Server

Davino, Cristina; Vistocco, Domenico

2013-01-01

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

Alternative regression models to assess increase in childhood BMI

Directory of Open Access Journals (Sweden)

Mansmann Ulrich

2008-09-01

Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Some Results on Mean Square Error for Factor Score Prediction

Science.gov (United States)

Krijnen, Wim P.

2006-01-01

For the confirmatory factor model a series of inequalities is given with respect to the mean square error (MSE) of three main factor score predictors. The eigenvalues of these MSE matrices are a monotonic function of the eigenvalues of the matrix gamma[subscript rho] = theta[superscript 1/2] lambda[subscript rho] 'psi[subscript rho] [superscript…

Logistic regression models

CERN Document Server

Hilbe, Joseph M

2009-01-01

This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

Logistic regression applied to natural hazards: rare event logistic regression with replications

Science.gov (United States)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Microwave-Assisted Synthesis of Red-Light Emitting Au Nanoclusters with the Use of Egg White

Science.gov (United States)

Tian, Jinghan; Yan, Lei; Sang, Aohua; Yuan, Hongyan; Zheng, Baozhan; Xiao, Dan

2014-01-01

We developed a simple, cost-effective, and eco-friendly method to synthesize gold nanoclusters (AuNCs) with red fluorescence. The experiment was performed using HAuCl[subscript 4], egg white, Na[subscript 2]CO[subscript 3] (known as soda ash or washing soda), and a microwave oven. In our experiment, fluorescent AuNCs were prepared within a…

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

Science.gov (United States)

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Understanding logistic regression analysis.

Science.gov (United States)

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

Three Contributions to Robust Regression Diagnostics

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2015-01-01

Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT

Minimax Regression Quantiles

DEFF Research Database (Denmark)

Bache, Stefan Holst

A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Logistic regression applied to natural hazards: rare event logistic regression with replications

Directory of Open Access Journals (Sweden)

M. Guns

2012-06-01

Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Determination of the Rotational Barrier for Kinetically Stable Conformational Isomers via NMR and 2D TLC: An Introductory Organic Chemistry Experiment

Science.gov (United States)

Rushton, Gregory T.; Burns, William G.; Lavin, Judi M.; Chong, Yong S.; Pellechia, Perry; Shimizu, Ken D.

2007-01-01

An experiment to determine the rotational barrier about a C[subscript aryl]-N[subscript imide] single bond that is suitable for first-semester organic chemistry students is presented. The investigation begins with the one-step synthesis of a N,N'-diaryl naphthalene diimide, which exists as two room temperature-stable atropisomers (syn and anti).…

Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

Science.gov (United States)

Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

2017-04-01

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive

A Dietary Pattern Derived by Reduced Rank Regression is Associated with Type 2 Diabetes in An Urban Ghanaian Population

Directory of Open Access Journals (Sweden)

Laura K. Frank

2015-07-01

Full Text Available Reduced rank regression (RRR is an innovative technique to establish dietary patterns related to biochemical risk factors for type 2 diabetes, but has not been applied in sub-Saharan Africa. In a hospital-based case-control study for type 2 diabetes in Kumasi (diabetes cases, 538; controls, 668 dietary intake was assessed by a specific food frequency questionnaire. After random split of our study population, we derived a dietary pattern in the training set using RRR with adiponectin, HDL-cholesterol and triglycerides as responses and 35 food items as predictors. This pattern score was applied to the validation set, and its association with type 2 diabetes was examined by logistic regression. The dietary pattern was characterized by a high consumption of plantain, cassava, and garden egg, and a low intake of rice, juice, vegetable oil, eggs, chocolate drink, sweets, and red meat; the score correlated positively with serum triglycerides and negatively with adiponectin. The multivariate-adjusted odds ratio of type 2 diabetes for the highest quintile compared to the lowest was 4.43 (95% confidence interval: 1.87–10.50, p for trend < 0.001. The identified dietary pattern increases the odds of type 2 diabetes in urban Ghanaians, which is mainly attributed to increased serum triglycerides.

37 CFR 382.2 - Royalty fees for the digital performance of sound recordings and the making of ephemeral...

Science.gov (United States)

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Royalty fees for the digital... SATELLITE DIGITAL AUDIO RADIO SERVICES Preexisting Subscription Services § 382.2 Royalty fees for the... monthly royalty fee for the public performance of sound recordings pursuant to 17 U.S.C. 114(d)(2) and the...

Modeling the Lithium Ion Battery

Science.gov (United States)

Summerfield, John

2013-01-01

The lithium ion battery will be a reliable electrical resource for many years to come. A simple model of the lithium ions motion due to changes in concentration and voltage is presented. The battery chosen has LiCoO[subscript 2] as the cathode, LiPF[subscript 6] as the electrolyte, and LiC[subscript 6] as the anode. The concentration gradient and…

A Dramatic Classroom Demonstration of Limiting Reagent Using the Vinegar and Sodium Hydrogen Carbonate Reaction

Science.gov (United States)

Artdej, Romklao; Thongpanchang, Tienthong

2008-01-01

This demonstration is designed to illustrate the concept of limiting reagent in a spectacular way. Via a series of experiments where the amount of vinegar is fixed and the amount of NaHCO[subscript 3] is gradually increased, the volume of CO[subscript 2] generated from the reaction varies corresponding to the amount of NaHCO[subscript 3] until it…

Post-processing through linear regression

Science.gov (United States)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Helping Students Assess the Relative Importance of Different Intermolecular Interactions

Science.gov (United States)

Jasien, Paul G.

2008-01-01

A semi-quantitative model has been developed to estimate the relative effects of dispersion, dipole-dipole interactions, and H-bonding on the normal boiling points ("T[subscript b]") for a subset of simple organic systems. The model is based upon a statistical analysis using multiple linear regression on a series of straight-chain organic…

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Better Autologistic Regression

Directory of Open Access Journals (Sweden)

Mark A. Wolters

2017-11-01

Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

Estimation of Chinese surface NO2 concentrations combining satellite data and Land Use Regression

Science.gov (United States)

Anand, J.; Monks, P.

2016-12-01

Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. In this work a new technique of estimating surface NO2 combining both satellite and in-situ data is presented, in which a Land Use Regression (LUR) model is used to create high resolution pollution maps based on known predictor variables such as population density, road networks, and land cover. By employing a mixed effects approach, it is possible to take advantage of the spatiotemporal variability in the satellite-derived column densities to account for daily and regional variations in surface NO2 caused by factors such as temperature, elevation, and wind advection. In this work, surface NO2 maps are modelled over the North China Plain and Pearl River Delta during high-pollution episodes by combining in-situ measurements and tropospheric columns from the Ozone Monitoring Instrument (OMI). The modelled concentrations show good agreement with in-situ data and surface NO2 concentrations derived from the MACC-II global reanalysis.

Weighted SGD for ℓp Regression with Randomized Preconditioning*

Science.gov (United States)

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.

2018-01-01

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems—e.g., ℓ2 and ℓ1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓp regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓp solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ1 regression with size n by d, pwSGD returns an approximate solution with ε relative error in the objective value in 𝒪(log n·nnz(A)+poly(d)/ε2) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in

Semiparametric regression during 2003–2007

KAUST Repository

Ruppert, David; Wand, M.P.; Carroll, Raymond J.

2009-01-01

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Science.gov (United States)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Linear regression

CERN Document Server

Olive, David J

2017-01-01

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

Regression modeling of ground-water flow

Science.gov (United States)

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Post-processing through linear regression

Directory of Open Access Journals (Sweden)

B. Van Schaeybroeck

2011-03-01

Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.

These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Yielding Unexpected Results: Precipitation of Ba[subscript3](PO[subscript4])[subscript2] and Implications for Teaching Solubility Principles in the General Chemistry Curriculum

Science.gov (United States)

Hazen, Jeffery L.; Cleary, David A.

2014-01-01

Precipitation of barium phosphate from aqueous solutions of a barium salt and a phosphate salt forms the basis for a number of conclusions drawn in general chemistry. For example, the formation of a solid white precipitate is offered as evidence that barium phosphate is insoluble. Furthermore, analysis of the supernatant is used to illustrate the…

Logistic regression applied to natural hazards: rare event logistic regression with replications

OpenAIRE

Guns, M.; Vanacker, Veerle

2012-01-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Deriving the Regression Line with Algebra

Science.gov (United States)

Quintanilla, John A.

2017-01-01

Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…

A Seemingly Unrelated Poisson Regression Model

OpenAIRE

King, Gary

1989-01-01

This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

Science.gov (United States)

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Recursive Algorithm For Linear Regression

Science.gov (United States)

Varanasi, S. V.

1988-01-01

Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

Standards for Standardized Logistic Regression Coefficients

Science.gov (United States)

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

Science.gov (United States)

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

Transient simulation of regression rate on thrust regulation process in hybrid rocket motor

Directory of Open Access Journals (Sweden)

Tian Hui

2014-12-01

Full Text Available The main goal of this paper is to study the characteristics of regression rate of solid grain during thrust regulation process. For this purpose, an unsteady numerical model of regression rate is established. Gas–solid coupling is considered between the solid grain surface and combustion gas. Dynamic mesh is used to simulate the regression process of the solid fuel surface. Based on this model, numerical simulations on a H2O2/HTPB (hydroxyl-terminated polybutadiene hybrid motor have been performed in the flow control process. The simulation results show that under the step change of the oxidizer mass flow rate condition, the regression rate cannot reach a stable value instantly because the flow field requires a short time period to adjust. The regression rate increases with the linear gain of oxidizer mass flow rate, and has a higher slope than the relative inlet function of oxidizer flow rate. A shorter regulation time can cause a higher regression rate during regulation process. The results also show that transient calculation can better simulate the instantaneous regression rate in the operation process.

Logistic regression for dichotomized counts.

Science.gov (United States)

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

[Application of detecting and taking overdispersion into account in Poisson regression model].

Science.gov (United States)

Bouche, G; Lepage, B; Migeot, V; Ingrand, P

2009-08-01

Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.

Efficacy of Intravitreal injection of 2-Methoxyestradiol in regression of neovascularization of a retinopathy of prematurity rat model.

Science.gov (United States)

Said, Azza Mohamed Ahmed; Zaki, Rania Gamal Eldin; Salah Eldin, Rania A; Nasr, Maha; Azab, Samar Saad; Elzankalony, Yaser Abdelmageuid

2017-04-04

Retinopathy of prematurity (ROP) is one of the targets for early detection and treatment to prevent childhood blindness in world health organization programs. The purpose of study was to evaluate the efficacy of intravitreal injection of 2-Methoxyestradiol (2-ME) nanoemulsion in regressing neovascularization of a ROP rat model. A prospective comparative case - control animal study conducted on 56 eyes of 28 healthy new born Sprague Dawley male albino rat. ROP was induced in 21 rats then two concentrations of 2-ME nanoparticles were injected in right eyes of 14 rats (low dose; study group I, high dose; study group II). A blank nanoemulsion was injected in the right eyes of seven rats (control positive group I). No injections performed in contralateral left eyes (control positive group II). Seven rats (14 eyes) were kept in room air (control negative group). On postnatal day 17, eyeballs were enucleated. Histological structure of the retina was examined using Hematoxylin and eosin staining. Vascular endothelial growth factor (VEGF) and glial fibrillary acidic protein (GFAP) expressions were detected by immunohistochemical studies. Intravitreal injection of 2-ME (in the two concentrations) caused marked regression of the new vascular tufts on the vitreal side with normal organization and thickness of the retina especially in study group II, which also show negative VEGF immunoreaction. Positive GFAP expression was detected in the control positive groups and study group (I). Intravitreal injection of 2-Methoxyestradiol nanoemulsion is a promising effective method in reduction of neovascularization of a ROP rat model.

Bayesian ARTMAP for regression.

Science.gov (United States)

Sasu, L M; Andonie, R

2013-10-01

Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mechanisms of neuroblastoma regression

Science.gov (United States)

Brodeur, Garrett M.; Bagatell, Rochelle

2014-01-01

Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

Continuous validation of ASTEC containment models and regression testing

International Nuclear Information System (INIS)

Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin

2014-01-01

The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

Science.gov (United States)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Panel Smooth Transition Regression Models

DEFF Research Database (Denmark)

González, Andrés; Terasvirta, Timo; Dijk, Dick van

We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

Thermal Efficiency Degradation Diagnosis Method Using Regression Model

International Nuclear Information System (INIS)

Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol

2011-01-01

This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

Impact of multicollinearity on small sample hydrologic regression models

Science.gov (United States)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Inter-Library Loans and Document Supply Services in Italy Appear to Supplement Journal Subscriptions Rather Than Replace Them. A Review of: Bernardini, E., & Mangiaracini, S. (2011. The relationship between ILL/document supply and journal subscriptions. Interlending and Document Supply, 39(1, 9-25. doi: 10.1108/02641611111112101

Directory of Open Access Journals (Sweden)

Kathryn Oxborrow

2011-01-01

Full Text Available Objectives – To examine patterns of Inter-Library Loans and Document Supply (ILL in a large network of libraries over a period of five years, in order to establish whether ILL services are being used to replace journal subscriptions. The authors also aimed to establish which journal titles were most requested in their network, to inform future acquisitions policy.Design – Longitudinal study using transactional data collected from the Italian Network for Inter-Library Document Exchange (NILDE for the period 2005-2009.Setting – The Italian library sector.Subjects – Member libraries of NILDE, which is the largest ILL network in Italy with several hundred libraries. These consist primarily of university libraries, but also hospitals and health research institutions, public research institutions, and not-for-profit and public organisations.Methods – ILL request data collected from the NILDE network software were analyzed. Figures were retrieved for the number of different journal titles requested per year of the study overall and by individual institution.Further analysis was undertaken on requests for more recent articles, those published up to five years prior to being requested. This involved creating a list of the most requested titles for each year, and then compiling a core collection of journals that were requested 20 or more times in each year of the study. These core titles were analyzed for trends by subject and publisher, and for any significant correlations between either Impact Factors (IFs or citation counts and ILL requests for particular journals.Main Results – The data revealed that the number of ILLs processed through NILDE increased every year during the period of the study. The majority of journals were only requested a small number of times in the five year period of the study, with 60% being requested five times or less. In the majority of instances, institutions were not borrowing the same title regularly. Analysis

Unbalanced Regressions and the Predictive Equation

DEFF Research Database (Denmark)

Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

Paradox of spontaneous cancer regression: implications for fluctuational radiothermy and radiotherapy

International Nuclear Information System (INIS)

Roy, Prasun K.; Dutta Majumder, D.; Biswas, Jaydip

1999-01-01

Spontaneous regression of malignant tumours without treatment is a most enigmatic phenomenon with immense therapeutic potentialities. We analyse such cases to find that the commonest cause is a preceding episode of high fever-induced thermal fluctuation which produce fluctuation of biochemical and immunological parameters. Using Prigogine-Glansdorff thermodynamic stability formalism and biocybernetic principles, we develop the theoretical foundation of tumour regression induced by thermal, radiational or oxygenational fluctuations. For regression, a preliminary threshold condition of fluctuations is derived, namely σ > 2.83. We present some striking confirmation of such fluctuation-induced regression of various therapy-resistant masses as Ewing tumour, neurogranuloma and Lewis lung carcinoma by utilising σ > 2.83. Our biothermodynamic stability model of malignancy appears to illuminate the marked increase of aggressiveness of mammalian malignancy which occurred around 250 million years ago when homeothermic warm-blooded pre-mammals evolved. Using experimental data, we propose a novel approach of multi-modal hyper-fluctuation therapy involving modulation of radiotherapeutic hyper-fractionation, temperature, radiothermy and immune-status. (author)

Autistic Regression

Science.gov (United States)

Matson, Johnny L.; Kozlowski, Alison M.

2010-01-01

Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…

Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation

Directory of Open Access Journals (Sweden)

Sharad Damodar Gore

2009-10-01

Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.

Evaluation of the Virtual Physiology of Exercise Laboratory Program

Science.gov (United States)

Dobson, John L.

2009-01-01

The Virtual Physiology of Exercise Laboratory (VPEL) program was created to simulate the test design, data collection, and analysis phases of selected exercise physiology laboratories. The VPEL program consists of four modules: (1) cardiovascular, (2) maximal O[subscript 2] consumption [Vo[subscript 2max], (3) lactate and ventilatory thresholds,…

Discriminative Elastic-Net Regularized Linear Regression.

Science.gov (United States)

Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

2017-03-01

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

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African Journals Online (AJOL)

Dr. I. I. Olatunji-Bello Nigerian Journal of Health and Biomedical Sciences Department of Physiology, College of Medicine of the University of Lagos P. M. B. 12003, Lagos NIGERIA Email: yemibello@lycos.com. Individual Nigeria - N1,000 per annum U. K. - 50 per annum U.S.A - $100 per annum. Canada - $80 per annum

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African Journals Online (AJOL)

School of Philosophy and Ethics University Of KwaZulu-Natal Durban 4041. South Africa. E-Mail: Clare@ukzn.ac.zaThis e-mail address is being protected from spambots. You need JavaScript enabled to view it. Members of the Philosophical Society of Southern Africa receive the Journal free of charge. ISSN: 0258-0136.

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African Journals Online (AJOL)

the International Council on Archives is published once a year with the technical support of the National Archives and Records Service of South Africa. However, the views expressed here are those of the authors. Articles appearing in the Journal are annotated, indexed or abstracted in African Journals OnLine (AJOL) at: ...

Regression-based Multi-View Facial Expression Recognition

NARCIS (Netherlands)

Rudovic, Ognjen; Patras, Ioannis; Pantic, Maja

2010-01-01

We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a

Methylphenidate and Atomoxetine Enhance Prefrontal Function through alpha[subscript 2]-Adrenergic and Dopamine D[subscript 1] Receptors

Science.gov (United States)

Gamo, Nao J.; Wang, Min; Arnsten, Amy F. T.

2010-01-01

Objective: This study examined the effects of the attention-deficit/hyperactivity disorder treatments, methylphenidate (MPH) and atomoxetine (ATM), on prefrontal cortex (PFC) function in monkeys and explored the receptor mechanisms underlying enhancement of PFC function at the behavioral and cellular levels. Method: Monkeys performed a working…

Categorical regression dose-response modeling

Science.gov (United States)

The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

Abstract Expression Grammar Symbolic Regression

Science.gov (United States)

Korns, Michael F.

This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.

Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

OpenAIRE

KELEŞ, Taliha; ALTUN, Murat

2016-01-01

Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

Pathological assessment of liver fibrosis regression

Directory of Open Access Journals (Sweden)

WANG Bingqiong

2017-03-01

Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

FBH1 Catalyzes Regression of Stalled Replication Forks

Directory of Open Access Journals (Sweden)

Kasper Fugger

2015-03-01

Full Text Available DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity, is required for early phosphorylation of ATM substrates such as CHK2 and CtIP as well as hyperphosphorylation of RPA. These phosphorylations occur prior to apparent DNA double-strand break formation. Furthermore, FBH1-dependent signaling promotes checkpoint control and preserves genome integrity. We propose a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks.

Logistic Regression: Concept and Application

Science.gov (United States)

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

Science.gov (United States)

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha

2014-12-08

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Sparse reduced-rank regression with covariance estimation

KAUST Repository

Chen, Lisha; Huang, Jianhua Z.

2014-01-01

Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data.

Science.gov (United States)

Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A

2015-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

Geographically weighted regression model on poverty indicator

Science.gov (United States)

Slamet, I.; Nugroho, N. F. T. A.; Muslich

2017-12-01

In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

Towards molecular design using 2D-molecular contour maps obtained from PLS regression coefficients

Science.gov (United States)

Borges, Cleber N.; Barigye, Stephen J.; Freitas, Matheus P.

2017-12-01

The multivariate image analysis descriptors used in quantitative structure-activity relationships are direct representations of chemical structures as they are simply numerical decodifications of pixels forming the 2D chemical images. These MDs have found great utility in the modeling of diverse properties of organic molecules. Given the multicollinearity and high dimensionality of the data matrices generated with the MIA-QSAR approach, modeling techniques that involve the projection of the data space onto orthogonal components e.g. Partial Least Squares (PLS) have been generally used. However, the chemical interpretation of the PLS-based MIA-QSAR models, in terms of the structural moieties affecting the modeled bioactivity has not been straightforward. This work describes the 2D-contour maps based on the PLS regression coefficients, as a means of assessing the relevance of single MIA predictors to the response variable, and thus allowing for the structural, electronic and physicochemical interpretation of the MIA-QSAR models. A sample study to demonstrate the utility of the 2D-contour maps to design novel drug-like molecules is performed using a dataset of some anti-HIV-1 2-amino-6-arylsulfonylbenzonitriles and derivatives, and the inferences obtained are consistent with other reports in the literature. In addition, the different schemes for encoding atomic properties in molecules are discussed and evaluated.

22 CFR 1102.2 - Definitions.

Science.gov (United States)

2010-04-01

... for examination and copying, or furnishing a copy of records. Duplication refers to the process of... organization, functions, policies, decisions, procedures, operations, or other activities of the Government or... make their products available for purchase or subscription by the general public. In the case of...

Transient Transport Experiments in the CDX-U Spherical Torus

International Nuclear Information System (INIS)

T. Munsat; P.C. Efthimion; B. Jones; R. Kaita; R. Majeski; D. Stutman; G. Taylor

2001-01-01

Electron transport has been measured in the Current Drive Experiment-Upgrade (CDX-U) using two separate perturbative techniques. Gas modulation at the plasma edge was used to introduce cold-pulses which propagate towards the plasma center, providing time-of-flight information leading to a determination of chi(subscript e) as a function of radius. Sawteeth at the q=1 radius (r/a ∼ 0.15) induced heat-pulses which propagated outward towards the plasma edge, providing a complementary time-of-flight based chi(subscript e) profile measurement. This work represents the first localized measurement of chi(subscript e) in a spherical torus. It is found that chi(subscript e) = 1-2 meters squared per second in the plasma core (r/a < 1/3), increasing by an order of magnitude or more outside of this region. Furthermore, the chi(subscript e) profile exhibits a sharp transition near r/a = 1/3. Spectral and profile analyses of the soft X-rays, scanning interferometer, and edge probe data show no evidence of a significant magnetic island causing the high chi(subscript e) region

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

Vavrin, J.

1989-01-01

The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

Cyclic codes of length 2 m

Indian Academy of Sciences (India)

Proceedings – Mathematical Sciences. Current Issue : Vol. 128, Issue 1 · Current Issue Volume 128 | Issue 1. March 2018. Home · Volumes & Issues · Special Issues · Forthcoming Articles · Search · Editorial Board · Information for Authors · Subscription ...

Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

2012-01-01

Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin

2017-01-19

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Testing discontinuities in nonparametric regression

KAUST Repository

Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun

2017-01-01

In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100

Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus

Science.gov (United States)

Hellack, Bryan; Sugiri, Dorothea; Schins, Roel P. F.; Schikowski, Tamara; Krämer, Ursula; Kuhlbusch, Thomas A. J.; Hoffmann, Barbara

2017-12-01

While land use regression models (LUR) are commonly used, e.g. for the prediction of spatially variable air pollutant mass concentrations, they are scarcely used for predicting the oxidative potential (OP), a suggested unifying predictor of health effects. Therefore a LUR model was developed to examine if long-term OP of fine particulate exposure can be reasonably predicted by LUR modeling and whether it is related to health effects in a study region comprised of urban and rural areas. Four 14-day sampling periods over 1 year at 40 sites in the western Ruhr Area and adjacent northern rural area, Germany, in 2002/2003 were conducted and annual Nitrogen Dioxide (NO2), fine particles (PM2.5), and OP were calculated. LUR models were developed to estimate spatially-resolved annual OP, NO2 and PM2.5 concentrations. The model performance was checked by leave-one-out cross validation (LOOCV) and cox regression was used to analyze the association of modeled residential OP and NO2 with incident type 2 diabetes mellitus (T2DM) in 1784 elderly women during a mean follow-up of 16 years (baseline 1985-1994). The measured OP and NO2 concentrations were moderately correlated (rSpearman 0.57). The LUR models explained 62% and 92% of the OP and NO2 variance (adjusted LOOCV R2 57% and 90%). PM10 emission from combustion in a 5000 m buffer was the most important predictor for OP and NO2. Modeled pollutants were highly correlated (rSpearman 0.87). Model quality for OP was sensitive to the inclusion of a single influential measurement site. For PM2.5 mass only an insufficient model with a low explained variance of 22% (adjusted R2) was developed so no health effects analyses were conducted with estimated PM2.5. Increases in OP and NO2 were associated with an increase in risk of T2DM by a hazard ratio of 1.38 (95% CI 1.06-1.80) and 1.39 (95% CI 1.07-1.81) per interquartile range of OP and NO2, respectively. We conclude that spatially-resolved OP can be predicted by LUR modeling, but

On Solving Lq-Penalized Regressions

Directory of Open Access Journals (Sweden)

Tracy Zhou Wu

2007-01-01

Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

Science.gov (United States)

Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

2008-02-18

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

Testing Heteroscedasticity in Robust Regression

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

Science.gov (United States)

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Spontaneous regression of a congenital melanocytic nevus

Directory of Open Access Journals (Sweden)

Amiya Kumar Nath

2011-01-01

Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.

Classroom Carbon Dioxide Concentration, School Attendance, and Educational Attainment

Science.gov (United States)

Gaihre, Santosh; Semple, Sean; Miller, Janice; Fielding, Shona; Turner, Steve

2014-01-01

Background: We tested the hypothesis that classroom carbon dioxide (CO[subscript 2]) concentration is inversely related to child school attendance and educational attainment. Methods: Concentrations of CO[subscript 2] were measured over a 3-5?day period in 60 naturally ventilated classrooms of primary school children in Scotland. Concentrations of…

Determinants of LSIL Regression in Women from a Colombian Cohort

International Nuclear Information System (INIS)

Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia

2010-01-01

Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.

12 CFR 271.2 - Definitions.

Science.gov (United States)

2010-01-01

... duplicating documents in response to a request made under § 271.5. (d) Duplication refers to the process of... Committee includes rules, statements, decisions, minutes, memoranda, letters, reports, transcripts, accounts... available for purchase or subscription by the general public. (3) “Freelance” journalists may be regarded as...

22 CFR 303.2 - Definitions.

Science.gov (United States)

2010-04-01

... before assigning the request to a category. (b) Duplication means the process of making a copy of a... as evidence of the organization, functions, policies, decisions, procedures, operations, or other... “news”) who make their products available for purchase or subscription by the general public. These...

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

Is past life regression therapy ethical?

Science.gov (United States)

Andrade, Gabriel

2017-01-01

Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.

Regression Models for Market-Shares

DEFF Research Database (Denmark)

Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue

2005-01-01

On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....

Regressing Atherosclerosis by Resolving Plaque Inflammation

Science.gov (United States)

2017-07-01

regression requires the alteration of macrophages in the plaques to a tissue repair “alternatively” activated state. This switch in activation state... tissue repair “alternatively” activated state. This switch in activation state requires the action of TH2 cytokines interleukin (IL)-4 or IL-13. To...regulation of tissue macrophage and dendritic cell population dynamics by CSF-1. J Exp Med. 2011;208(9):1901–1916. 35. Xu H, Exner BG, Chilton PM

A Twist on Measuring Catalase

Science.gov (United States)

Bryer, Pamela

2016-01-01

"Catalase," an enzyme found in both plant and animal cells, prevents the accumulation of toxic levels of hydrogen peroxide (H[subscript 2]O[subscript 2]) by catalyzing its decomposition to water and oxygen gas. Because this enzyme is ubiquitous, it is frequently used in high school biology laboratories to explore enzyme reactions. This…

Mobile Air Monitoring: Measuring Change in Air Quality in the City of Hamilton, 2005-2010

Science.gov (United States)

Adams, Matthew D.; DeLuca, Patrick F.; Corr, Denis; Kanaroglou, Pavlos S.

2012-01-01

This paper examines the change in air pollutant concentrations between 2005 and 2010 occurring in the City of Hamilton, Ontario, Canada. After analysis of stationary air pollutant concentration data, we analyze mobile air pollutant concentration data. Air pollutants included in the analysis are CO, PM[subscript 2.5], SO[subscript 2], NO,…

Synthesis of an Albendazole Metabolite: Characterization and HPLC Determination

Science.gov (United States)

Mahler, Graciela; Davyt, Danilo; Gordon, Sandra; Incerti, Marcelo; Nunez, Ivana; Pezaroglo, Horacio; Scarone, Laura; Serra, Gloria; Silvera, Mauricio; Manta, Eduardo

2008-01-01

In this laboratory activity, students are introduced to the synthesis of an albendazole metabolite obtained by a sulfide oxidation reaction. Albendazole as well as its metabolite, albendazole sulfoxide, are used as anthelmintic drugs. The oxidation reagent is H[subscript 2]O[subscript 2] in acetic acid. The reaction is environmental friendly,…

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Score Normalization using Logistic Regression with Expected Parameters

NARCIS (Netherlands)

Aly, Robin

State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression. Experiments on the Gov2 and CluewebA collection indicate that our method is

Speckle tracking echocardiography derived 2-dimensional myocardial strain predicts left ventricular function and mass regression in aortic stenosis patients undergoing aortic valve replacement.

Science.gov (United States)

Staron, Adam; Bansal, Manish; Kalakoti, Piyush; Nakabo, Ayumi; Gasior, Zbigniew; Pysz, Piotr; Wita, Krystian; Jasinski, Marek; Sengupta, Partho P

2013-04-01

Regression of left ventricular (LV) mass in severe aortic stenosis (AS) following aortic valve replacement (AVR) reduces the potential risk of sudden death and congestive heart failure associated with LV hypertrophy. We investigated whether abnormalities of resting LV deformation in severe AS can predict the lack of regression of LV mass following AVR. Two-dimensional speckle tracking echocardiography (STE) was performed in a total of 100 subjects including 60 consecutive patients with severe AS having normal LV ejection fraction (EF > 50 %) and 40 controls. STE was performed preoperatively and at 4 months following AVR, including longitudinal strain assessed from the apical 4-chamber and 2-chamber views and the circumferential and rotational mechanics measured from the apical short axis view. In comparison with controls, the patients with AS showed a significantly lower LV longitudinal (p regression (>10 %) following AVR. In conclusion, STE can quantify the burden of myocardial dysfunction in patients with severe AS despite the presence of normal LV ejection fraction. Furthermore, resting abnormalities in circumferential strain at LV apex is related with a hemodynamic milieu associated with the lack of LV mass regression during short-term follow up after AVR.

Development of ε-insensitive smooth support vector regression for predicting minimum miscibility pressure in CO2 flooding

Directory of Open Access Journals (Sweden)

Shahram Mollaiy-Berneti

2018-02-01

Full Text Available Successful design of a carbon dioxide (CO2 flooding in enhanced oil recovery projects mostly depends on accurate determination of CO2-crude oil minimum miscibility pressure (MMP. Due to the high expensive and time-consuming of experimental determination of MMP, developing a fast and robust method to predict MMP is necessary. In this study, a new method based on ε-insensitive smooth support vector regression (ε-SSVR is introduced to predict MMP for both pure and impure CO2 gas injection cases. The proposed ε-SSVR is developed using dataset of reservoir temperature, crude oil composition and composition of injected CO2. To serve better understanding of the proposed, feed-forward neural network and radial basis function network applied to denoted dataset. The results show that the suggested ε-SSVR has acceptable reliability and robustness in comparison with two other models. Thus, the proposed method can be considered as an alternative way to monitor the MMP in miscible flooding process.

76 FR 36107 - Combined Notice of Filings #2

Science.gov (United States)

2011-06-21

...: SET Cancellation of Reactive Power Tariff to be effective 6/15/ 2011. Filed Date: 06/15/2011... Reference Room in Washington, DC. There is an eSubscription link on the Web site that enables subscribers to...

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Regression periods in infancy: a case study from Catalonia.

Science.gov (United States)

Sadurní, Marta; Rostan, Carlos

2002-05-01

Based on Rijt-Plooij and Plooij's (1992) research on emergence of regression periods in the first two years of life, the presence of such periods in a group of 18 babies (10 boys and 8 girls, aged between 3 weeks and 14 months) from a Catalonian population was analyzed. The measurements were a questionnaire filled in by the infants' mothers, a semi-structured weekly tape-recorded interview, and observations in their homes. The procedure and the instruments used in the project follow those proposed by Rijt-Plooij and Plooij. Our results confirm the existence of the regression periods in the first year of children's life. Inter-coder agreement for trained coders was 78.2% and within-coder agreement was 90.1%. In the discussion, the possible meaning and relevance of regression periods in order to understand development from a psychobiological and social framework is commented upon.

Regression analysis using dependent Polya trees.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

The use of Meta-Regression Analysis to harmonize LCA literature: an application to GHG emissions of 2. and 3. generation biofuels

International Nuclear Information System (INIS)

Menten, Fabio; Cheze, Benoit; Patouillard, Laure; Bouvart, Frederique

2013-01-01

This article presents the results of a literature review performs with a meta-regression analysis (MRA) that focuses on the estimates of advanced biofuel Greenhouse Gas (GHG) emissions assessed with a Life Cycle Assessment (LCA) approach. The mean GHG emissions of both second (G2) and third generation (G3) biofuels and the effects of factors influencing these estimates are identified and quantified by means of specific statistical methods. 47 LCA studies are included in the database, providing 593 estimates. Each study estimate of the database is characterized by i) technical data/characteristics, ii) author's methodological choices and iii) typology of the study under consideration. The database is composed of both the vector of these estimates - expressed in grams of CO 2 equivalent per MJ of biofuel (g CO 2 eq/MJ) - and a matrix containing vectors of predictor variables which can be continuous or dummy variables. The former is the dependent variable while the latter corresponds to the explanatory variables of the meta-regression model. Parameters are estimated by mean of econometrics methods. Our results clearly highlight a hierarchy between G3 and G2 biofuels: life cycle GHG emissions of G3 biofuels are statistically higher than those of Ethanol which, in turn, are superior to those of BtL. Moreover, this article finds empirical support for many of the hypotheses formulated in narrative literature surveys concerning potential factors which may explain estimates variations. Finally, the MRA results are used to address the harmonization issue in the field of advanced biofuels GHG emissions thanks to the technique of benefits transfer using meta-regression models. The range of values hence obtained appears to be lower than the fossil fuel reference (about 83.8 in g CO 2 eq/ MJ). However, only Ethanol and BtL do comply with the GHG emission reduction thresholds for biofuels defined in both the American and European directives. (authors)

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Class and Home Problems: Carbon Dioxide Capture from Coal-Fired Power Plants Using Calcium Looping

Science.gov (United States)

Deshpande, Niranjani; Phalak, Nihar; Fan, Liang-Shih; Sundaresan, Sankaran

2015-01-01

Calcium looping is based on the simple premise of the reversible reaction between CO[subscript 2] and CaO. This reaction can be used for separation of CO2 from a mixture of gases; most notably the technology finds applications in CO[subscript 2] removal from gas streams in fossil fuel-based energy systems. This article gives a brief overview of…

The Physics behind a Simple Demonstration of the Greenhouse Effect

Science.gov (United States)

Buxton, Gavin A.

2014-01-01

A simple, and popular, demonstration of the greenhouse effect involves a higher temperature being observed in a container with an elevated concentration of CO[subscript 2] inside than in a container with just air enclosed, when subject to direct light. The CO[subscript 2] absorbs outgoing thermal radiation and causes the air inside the container…

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Science.gov (United States)

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Regression of environmental noise in LIGO data

International Nuclear Information System (INIS)

Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

2015-01-01

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

Quantum algorithm for linear regression

Science.gov (United States)

Wang, Guoming

2017-07-01

We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

Impact of aortic prosthesis-patient mismatch on left ventricular mass regression.

Science.gov (United States)

Alassal, Mohamed A; Ibrahim, Bedir M; Elsadeck, Nabil

2014-06-01

Prostheses used for aortic valve replacement may be small in relation to body size, causing prosthesis-patient mismatch and delaying left ventricular mass regression. This study examined the effect of prosthesis-patient mismatch on regression of left ventricular mass after aortic valve replacement. We prospectively studied 96 patients undergoing aortic valve replacement between 2007 and 2012. Mean and peak gradients and indexed effective orifice area were measured by transthoracic echocardiography at 3 and 6 months postoperatively. Patient-prosthesis mismatch was defined as indexed effective orifice area ≤0.85 cm(2)·m(-2). Moderate prosthesis-patient mismatch was present in 25% of patients. There were no significant differences in demographic and operative data between patients with and without prosthesis-patient mismatch. Left ventricular dimensions, posterior wall thickness, transvalvular gradients, and left ventricular mass decreased significantly after aortic valve replacement in both groups. The interventricular septal diameter and left ventricular mass index regression, and left ventricular ejection fraction were better in patients without prosthesis-patient mismatch. There was a significant positive correlation between the postoperative indexed effective orifice area of each valve prosthesis and the rate of left ventricular mass regression. Prosthesis-patient mismatch leads to higher transprosthetic gradients and impaired left ventricular mass regression. A small-sized valve prosthesis does not necessarily result in prosthesis-patient mismatch, and may be perfectly adequate in patient with small body size. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Forecasting with Dynamic Regression Models

CERN Document Server

Pankratz, Alan

2012-01-01

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

Biostatistics Series Module 6: Correlation and Linear Regression.

Science.gov (United States)

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

Vasak, F.; Epstein, N.

1996-01-01

A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

Heterogeneous Catalysis: Deuterium Exchange Reactions of Hydrogen and Methane

Science.gov (United States)

Mirich, Anne; Miller, Trisha Hoette; Klotz, Elsbeth; Mattson, Bruce

2015-01-01

Two gas phase deuterium/hydrogen exchange reactions are described utilizing a simple inexpensive glass catalyst tube containing 0.5% Pd on alumina through which gas mixtures can be passed and products collected for analysis. The first of these exchange reactions involves H[subscript 2] + D[subscript 2], which proceeds at temperatures as low as 77…

The Effectiveness of Hypochlorous Acid Solution on Healing of Infected Diabetic Foot Ulcers

Science.gov (United States)

Ragab, Islam I.; Kamal, Ahmed

2017-01-01

Wound cleansing remains a corner stone in the management of diabetic foot ulcer. Hydrogen Peroxide (H[subscript 2]O[subscript 2]) and Povidone Iodine are topical antimicrobial agents but known to be toxic to cells involved in the wound healing cascade. The biggest challenge for the physicians and nurses is searching for a safe, noncytotoxic and…

Ocean Acidification: Hands-On Experiments to Explore the Causes and Consequences

Science.gov (United States)

Bruno, Barbara C.; Tice, Kimberly A.; Puniwai, Noelani; Achilles, Kate

2011-01-01

Ocean acidification is one of the most serious environmental issues facing the planet (e.g., Doney 2006; Guinotte and Fabry 2009). It is caused by excess carbon dioxide (CO[subscript 2]) in the atmosphere. Human activities such as burning fossil fuels put CO[subscript 2] and other heat-trapping gases into the atmosphere, which causes the Earth's…

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Directory of Open Access Journals (Sweden)

Qiutong Jin

2016-06-01

Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

Degradation of Environmental Contaminants with Water-Soluble Cobalt Catalysts: An Integrative Inorganic Chemistry Investigation

Science.gov (United States)

Evans, Alexandra L.; Messersmith, Reid E.; Green, David B.; Fritsch, Joseph M.

2011-01-01

We present an integrative laboratory investigation incorporating skills from inorganic chemistry, analytical instrumentation, and physical chemistry applied to a laboratory-scale model of the environmental problem of chlorinated ethylenes in groundwater. Perchloroethylene (C[subscript 2]Cl[subscript 4], PCE) a common dry cleaning solvent,…

Intermediate and advanced topics in multilevel logistic regression analysis.

Science.gov (United States)

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

Science.gov (United States)

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (Plogistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

Science.gov (United States)

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

Gibrat’s law and quantile regressions

DEFF Research Database (Denmark)

Distante, Roberta; Petrella, Ivan; Santoro, Emiliano

2017-01-01

The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...

ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES

NARCIS (Netherlands)

RUSCHENDORF, L; DEVALK, [No Value

We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive

Introduction to the use of regression models in epidemiology.

Science.gov (United States)

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

From Rasch scores to regression

DEFF Research Database (Denmark)

Christensen, Karl Bang

2006-01-01

Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....

Producing The New Regressive Left

DEFF Research Database (Denmark)

Crone, Christine

members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...

Face Alignment via Regressing Local Binary Features.

Science.gov (United States)

Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

2016-03-01

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

Mixture of Regression Models with Single-Index

OpenAIRE

Xiang, Sijia; Yao, Weixin

2016-01-01

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

Local bilinear multiple-output quantile/depth regression

Czech Academy of Sciences Publication Activity Database

Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

2015-01-01

Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

Science.gov (United States)

Enders, Felicity

2013-12-01

Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

Correlation between tumor regression grade and rectal volume in neoadjuvant concurrent chemoradiotherapy for rectal cancer

Energy Technology Data Exchange (ETDEWEB)

Lee, Hong Seok; Choi, Doo Ho; Park, Hee Chul; Park, Won; Yu, Jeong Il; Chung, Kwang Zoo [Dept. of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2016-09-15

To determine whether large rectal volume on planning computed tomography (CT) results in lower tumor regression grade (TRG) after neoadjuvant concurrent chemoradiotherapy (CCRT) in rectal cancer patients. We reviewed medical records of 113 patients treated with surgery following neoadjuvant CCRT for rectal cancer between January and December 2012. Rectal volume was contoured on axial images in which gross tumor volume was included. Average axial rectal area (ARA) was defined as rectal volume divided by longitudinal tumor length. The impact of rectal volume and ARA on TRG was assessed. Average rectal volume and ARA were 11.3 mL and 2.9 cm². After completion of neoadjuvant CCRT in 113 patients, pathologic results revealed total regression (TRG 4) in 28 patients (25%), good regression (TRG 3) in 25 patients (22%), moderate regression (TRG 2) in 34 patients (30%), minor regression (TRG 1) in 24 patients (21%), and no regression (TRG0) in 2 patients (2%). No difference of rectal volume and ARA was found between each TRG groups. Linear correlation existed between rectal volume and TRG (p = 0.036) but not between ARA and TRG (p = 0.058). Rectal volume on planning CT has no significance on TRG in patients receiving neoadjuvant CCRT for rectal cancer. These results indicate that maintaining minimal rectal volume before each treatment may not be necessary.

Correlation between tumor regression grade and rectal volume in neoadjuvant concurrent chemoradiotherapy for rectal cancer

International Nuclear Information System (INIS)

Lee, Hong Seok; Choi, Doo Ho; Park, Hee Chul; Park, Won; Yu, Jeong Il; Chung, Kwang Zoo

2016-01-01

To determine whether large rectal volume on planning computed tomography (CT) results in lower tumor regression grade (TRG) after neoadjuvant concurrent chemoradiotherapy (CCRT) in rectal cancer patients. We reviewed medical records of 113 patients treated with surgery following neoadjuvant CCRT for rectal cancer between January and December 2012. Rectal volume was contoured on axial images in which gross tumor volume was included. Average axial rectal area (ARA) was defined as rectal volume divided by longitudinal tumor length. The impact of rectal volume and ARA on TRG was assessed. Average rectal volume and ARA were 11.3 mL and 2.9 cm². After completion of neoadjuvant CCRT in 113 patients, pathologic results revealed total regression (TRG 4) in 28 patients (25%), good regression (TRG 3) in 25 patients (22%), moderate regression (TRG 2) in 34 patients (30%), minor regression (TRG 1) in 24 patients (21%), and no regression (TRG0) in 2 patients (2%). No difference of rectal volume and ARA was found between each TRG groups. Linear correlation existed between rectal volume and TRG (p = 0.036) but not between ARA and TRG (p = 0.058). Rectal volume on planning CT has no significance on TRG in patients receiving neoadjuvant CCRT for rectal cancer. These results indicate that maintaining minimal rectal volume before each treatment may not be necessary

The MIDAS Touch: Mixed Data Sampling Regression Models

OpenAIRE

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

2004-01-01

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Suppression Situations in Multiple Linear Regression

Science.gov (United States)

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Significance testing in ridge regression for genetic data

Directory of Open Access Journals (Sweden)

De Iorio Maria

2011-09-01

Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor

2012-06-29

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Regression calibration with more surrogates than mismeasured variables

KAUST Repository

Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.

2012-01-01

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Inclusão do equivalente energético do lactato na regressão VO2-intensidade em corrida horizontal e inclinada (10,5%

Directory of Open Access Journals (Sweden)

Victor Machado REIS

Full Text Available Resumo O estudo teve por objetivo analisar o efeito da adição do equivalente energético do lactato sanguíneo com a medida de VO2 durante a corrida em esteira horizontal (0% e inclinada (10,5%, como forma de estimativa do custo energético da corrida. Treze corredores de meia e longa distância (idade 28,1 ± 4,2 anos; estatura 1,75 ± 0,07 m; massa corporal 65,2 ± 4,9 kg; VO2max 70,3 ± 4,9 ml·kg-1·min-1 cumpriram dois testes em esteira rolante (0% e 10,5% que incluíram vários estágios em intensidade constante. Foram calculadas para cada atleta as regressões VO2-velocidade, bem como regressões alternativas com a adição de um equivalente energético de 3 ml O2 Eq·kg-1·mM [La-] às medições de VO2. Não se verificou interação significativa entre a adição do equivalente do lactato e a inclinação da esteira. A ANOVA indicou um efeito significativo da adição do equivalente do lactato na inclinação da reta de regressão e na estimativa do custo energético. Os tamanhos do efeito obtidos indicam que este efeito é mais forte na corrida horizontal. Estes resultados sugerem que em testes laboratoriais com corredores treinados se deverá considerar a adição dos valores de VO2 com os equivalentes energéticos do lactato.

Few crystal balls are crystal clear : eyeballing regression

International Nuclear Information System (INIS)

Wittebrood, R.T.

1998-01-01

The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

Structured Additive Regression Models: An R Interface to BayesX

Directory of Open Access Journals (Sweden)

Nikolaus Umlauf

2015-02-01

Full Text Available Structured additive regression (STAR models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms, fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.

Impacts on CO2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme

Directory of Open Access Journals (Sweden)

Jie Zhang

2016-11-01

Full Text Available A pilot regional carbon emission trading scheme (ETS has been implemented in China for more than two years. An investigation into the impacts of different factors on carbon dioxide (CO2 emission allowance prices provides guidance for price-making in 2017 when the nation-wide ETS of China will be established. This paper adopts a quantile regression approach to estimate the impacts of different factors in Shanghai emission trading scheme (SH-ETS, namely, economic growth, energy prices and temperature. The empirical analysis shows that: (i the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS or quantile regression method; (iii a long-run negative relationship exists between the coal price and the Shanghai emission allowances (SHEA prices, but a positive interaction under different quantiles, especially the 25%–50% quantiles; (iv temperature has a significantly positive effect at the 20%–30% quantiles and a conspicuous negative impact at the right tail of the allowances prices.

Programming Amazon Web Services S3, EC2, SQS, FPS, and SimpleDB

CERN Document Server

Murty, James

2009-01-01

With this book, you'll learn how companies can take advantage of Amazon Web Services (AWS) to rent" computing power, data storage and bandwidth on Amazon's vast network infrastructure. Programming Amazon Web Services gives developers the background and technical detail they need for using Amazon's subscription-based Simple Storage Service (S3), Elastic Compute Cloud (EC2), Simple Queue Service (SQS), Flexible Payments Service (FPS), and SimpleDB to build web-scale business applications. "

Regression methods for medical research

CERN Document Server

Tai, Bee Choo

2013-01-01

Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

Should metacognition be measured by logistic regression?

Science.gov (United States)

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Beyond [lambda][subscript max] Part 2: Predicting Molecular Color

Science.gov (United States)

Williams, Darren L.; Flaherty, Thomas J.; Alnasleh, Bassam K.

2009-01-01

A concise roadmap for using computational chemistry programs (i.e., Gaussian 03W) to predict the color of a molecular species is presented. A color-predicting spreadsheet is available with the online material that uses transition wavelengths and peak-shape parameters to predict the visible absorbance spectrum, transmittance spectrum, chromaticity…

Spontaneous regression of residual low-grade cerebellar pilocytic astrocytomas in children

International Nuclear Information System (INIS)

Gunny, Roxana S.; Saunders, Dawn E.; Hayward, Richard D.; Phipps, Kim P.; Harding, Brian N.

2005-01-01

Cerebellar low-grade astrocytomas (CLGAs) of childhood are benign tumours and are usually curable by surgical resection alone or combined with adjuvant radiotherapy. To undertake a retrospective study of our children with CLGA to determine the optimum schedule for surveillance imaging following initial surgery. In this report we describe the phenomenon of spontaneous regression of residual tumour and discuss its prognostic significance regarding future imaging. A retrospective review was conducted of children treated for histologically proven CLGA at Great Ormond Street Hospital from 1988 to 1998. Of 83 children with CLGA identified, 13 (15.7%) had incomplete resections. Two children with large residual tumours associated with persistent symptoms underwent additional treatment. Eleven children were followed by surveillance imaging alone for a mean of 6.83 years (range 2-13.25 years). Spontaneous tumour regression was seen in 5 (45.5%) of the 11 children. There were no differences in age, gender, symptomatology, histological grade or Ki-67 fractions between those with spontaneous tumour regression and those with progression. There was a non-significant trend that larger volume residual tumours progressed. Residual tumour followed by surveillance imaging may either regress or progress. For children with residual disease we recommend surveillance imaging every 6 months for the first 2 years, every year for years 3, 4 and 5, then every second year if residual tumour is still present 5 years after initial surgery. This would detect not only progressive or recurrent disease, but also spontaneous regression which can occur later than disease progression. (orig.)

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Developing and testing a global-scale regression model to quantify mean annual streamflow

Science.gov (United States)

Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.

2017-01-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.

Regression Analysis and the Sociological Imagination

Science.gov (United States)

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

International Nuclear Information System (INIS)

Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

2009-01-01

Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

Application of Stark Tuned Laser for Interferometry and Polarimetry in Plasmas

International Nuclear Information System (INIS)

H.K. Park; K.C. Lee; B. Deng; C.W. Domier; M. Johnson; B. Nathan; N.C. Luhmann, Jr.

2001-01-01

A Stark-tuned optically pumped far-infrared CH(subscript ''3'')OH laser at 119 mm has been successfully applied in the Far Infrared Tangential Interferometer/Polarimeter (FIReTIP) system for the National Spherical Torus Experiment (NSTX). The system will provide temporally and radially resolved 2-D electron density profile [n(subscript ''e'')(r,t)] and toroidal field profile [B(subscript ''T'')(r,t)] data. In the 2001 campaign, a single channel interferometer system has been operated and tested for the Faraday rotation measurement. A plan for improvement and upgrading of the FIReTIP is discussed

An Examination of the Counting Talk Test's Ability to Discern Individual Variability in Exercise-Induced Metabolic Stress

Science.gov (United States)

Bonafiglia, Jacob T.; Sawula, Laura J.; Gurd, Brendon J.

2018-01-01

The purpose of this study was to determine if the counting talk test can be used to discern whether an individual is exercising above or at/below maximal lactate steady state. Twenty-two participants completed VO[subscript 2]peak and counting talk test incremental step tests followed by an endurance test at 65% of work rate at VO[subscript 2]peak…

Measuring Steady-State Oxygen Uptake during the 6-Min Walk Test in Adults with Cerebral Palsy: Feasibility and Construct Validity

Science.gov (United States)

Maltais, Desiree B.; Robitaille, Nancy-Michelle; Dumas, Francine; Boucher, Normand; Richards, Carol L.

2012-01-01

This study evaluated the feasibility of measuring steady-state oxygen uptake (V[Combining Dot Above]O[subscript 2]) during the 6-min walk test (6MWT) in adults with cerebral palsy (CP) who walk without support and whether there is construct validity for net 6MWT V[Combining Dot Above]O[subscript 2] as a measure of their walking ability.…

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

Adaptive kernel regression for freehand 3D ultrasound reconstruction

Science.gov (United States)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

Predictors and overestimation of recalled mobile phone use among children and adolescents.

Science.gov (United States)

Aydin, Denis; Feychting, Maria; Schüz, Joachim; Andersen, Tina Veje; Poulsen, Aslak Harbo; Prochazka, Michaela; Klæboe, Lars; Kuehni, Claudia E; Tynes, Tore; Röösli, Martin

2011-12-01

A growing body of literature addresses possible health effects of mobile phone use in children and adolescents by relying on the study participants' retrospective reconstruction of mobile phone use. In this study, we used data from the international case-control study CEFALO to compare self-reported with objectively operator-recorded mobile phone use. The aim of the study was to assess predictors of level of mobile phone use as well as factors that are associated with overestimating own mobile phone use. For cumulative number and duration of calls as well as for time since first subscription we calculated the ratio of self-reported to operator-recorded mobile phone use. We used multiple linear regression models to assess possible predictors of the average number and duration of calls per day and logistic regression models to assess possible predictors of overestimation. The cumulative number and duration of calls as well as the time since first subscription of mobile phones were overestimated on average by the study participants. Likelihood to overestimate number and duration of calls was not significantly different for controls compared to cases (OR=1.1, 95%-CI: 0.5 to 2.5 and OR=1.9, 95%-CI: 0.85 to 4.3, respectively). However, likelihood to overestimate was associated with other health related factors such as age and sex. As a consequence, such factors act as confounders in studies relying solely on self-reported mobile phone use and have to be considered in the analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

riskRegression

DEFF Research Database (Denmark)

Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

2017-01-01

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

Science.gov (United States)

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

11 CFR 300.2 - Definitions.

Science.gov (United States)

2010-01-01

... donations to, an organization that is described in 26 U.S.C 501(c) and exempt from taxation under 26 U.S.C... any donations to, an organization that is described in 26 U.S.C. 501(c) and exempt from taxation under...) Donation. For purposes of part 300, donation means a payment, gift, subscription, loan, advance, deposit...

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

A quantile regression analysis of China's provincial CO_2 emissions: Where does the difference lie?

International Nuclear Information System (INIS)

Xu, Bin; Lin, Boqiang

2016-01-01

China is already the largest carbon dioxide emitter in the world. This paper adopts provincial panel data from 1990 to 2014 and employs quantile regression model to investigate the influencing factors of China's CO_2 emissions. The results show that economic growth plays a dominant role in the growth of CO_2 emissions due to massive fixed–asset investment and export trade. The influences of energy intensity on the lower 10th and upper 90th quantile provinces are stronger than those in the 25th–50th quantile provinces because of big differences in R&D expenditure and human resources distribution. The impact of urbanization increases continuously from the lower 10th quantile provinces to the 10th–25th, 25th–50th, 50th–75th, 75th–90th and upper 90th quantile provinces, owing to the differences in R&D personnel, real estate development and motor–vehicle ownership. The effect of industrialization on the upper 90th quantile provinces is greater than those on other quantile provinces on account of the differences in the industrial scale and the development of the building industry. Thus, the heterogeneity effects of influencing factors on different quantile provinces should be taken into consideration when discussing the mitigation of CO_2 emissions in China. - Highlights: • The driving forces of China's CO_2 emissions are investigated. • Economic growth plays a dominant role in the growth of CO_2 emissions. • The impact of urbanization increases from the lower 10th quantiles to the upper 90th quantiles.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Science.gov (United States)

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Modified Regression Rate Formula of PMMA Combustion by a Single Plane Impinging Jet

Directory of Open Access Journals (Sweden)

Tsuneyoshi Matsuoka

2017-01-01

Full Text Available A modified regression rate formula for the uppermost stage of CAMUI-type hybrid rocket motor is proposed in this study. Assuming a quasi-steady, one-dimensional, an energy balance against a control volume near the fuel surface is considered. Accordingly, the regression rate formula which can calculate the local regression rate by the quenching distance between the flame and the regression surface is derived. An experimental setup which simulates the combustion phenomenon involved in the uppermost stage of a CAMUI-type hybrid rocket motor was constructed and the burning tests with various flow velocities and impinging distances were performed. A PMMA slab of 20 mm height, 60 mm width, and 20 mm thickness was chosen as a sample specimen and pure oxygen and O2/N2 mixture (50/50 vol.% were employed as the oxidizers. The time-averaged regression rate along the fuel surface was measured by a laser displacement sensor. The quenching distance during the combustion event was also identified from the observation. The comparison between the purely experimental and calculated values showed good agreement, although a large systematic error was expected due to the difficulty in accurately identifying the quenching distance.

Facile synthesis of soybean phospholipid-encapsulated MoS2 nanosheets for efficient in vitro and in vivo photothermal regression of breast tumor

Directory of Open Access Journals (Sweden)

Li X

2016-04-01

Full Text Available Xiang Li,1 Yun Gong,2,3 Xiaoqian Zhou,1 Hui Jin,1 Huanhuan Yan,1 Shige Wang,2 Jun Liu11Department of Breast-Thyroid Surgery, Shanghai General Hospital of Nanjing Medical University, Shanghai, People’s Republic of China; 2College of Science, University of Shanghai for Science and Technology, 3Shanghai Publishing and Printing College, Shanghai, People’s Republic of ChinaAbstract: Two-dimensional MoS2 nanosheet has been extensively explored as a photothermal agent for tumor regression; however, its surface modification remains a great challenge. Herein, as an alternative to surface polyethylene glycol modification (PEGylation, a facile approach based on “thin-film” strategy has been proposed for the first time to produce soybean phospholipid-encapsulated MoS2 (SP-MoS2 nanosheets. By simply vacuum-treating MoS2 nanosheets/soybean phospholipid/chloroform dispersion in a rotary evaporator, SP-MoS2 nanosheet was successfully constructed. Owing to the steric hindrance of polymer chains, the surface-coated soybean phospholipid endowed MoS2 nanosheets with excellent colloidal stability. Without showing detectable in vitro and in vivo hemolysis, coagulation, and cyto-/histotoxicity, the constructed SP-MoS2 nanosheets showed good photothermal conversion performance and photothermal stability. SP-MoS2 nanosheet was shown to be a promising platform for in vitro and in vivo breast tumor photothermal therapy. The produced SP-MoS2 nanosheets featured low cost, simple fabrication, and good in vivo hemo-/histocompatibility and hold promising potential for future clinical tumor therapy.Keywords: soybean phospholipid, MoS2 nanosheets, in vivo, photothermal regression, breast tumor

General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.

Science.gov (United States)

Chung, Wooyong; Kim, Jisu; Lee, Heejin; Kim, Euntai

2015-11-01

Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

Computing multiple-output regression quantile regions

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2012-01-01

Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf

Diagrams for certain quotients of PSL (2, Z [i])

Indian Academy of Sciences (India)

Proceedings – Mathematical Sciences. Current Issue : Vol. 128, Issue 1. Current Issue Volume 128 | Issue 1. March 2018. Home · Volumes & Issues · Special Issues · Forthcoming Articles · Search · Editorial Board · Information for Authors · Subscription ...

Preface to Berk's "Regression Analysis: A Constructive Critique"

OpenAIRE

de Leeuw, Jan

2003-01-01

It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

Using Conductivity Measurements to Determine the Identities and Concentrations of Unknown Acids: An Inquiry Laboratory Experiment

Science.gov (United States)

Smith, K. Christopher; Garza, Ariana

2015-01-01

This paper describes a student designed experiment using titrations involving conductivity measurements to identify unknown acids as being either HCl or H[subscript 2]SO[subscript 4], and to determine the concentrations of the acids, thereby improving the utility of standard acid-base titrations. Using an inquiry context, students gain experience…

Topical versus Chronological Organization of Lifespan Development: Does It Make a Difference in Student Retention and Understanding?

Science.gov (United States)

Spangler, Brooke R.; Kiel, Elizabeth J.

2015-01-01

This study aimed to determine whether taking a chronological approach (CA) or topical approach (TA) to teaching developmental psychology resulted in different learning outcomes. Across two semesters, in four classes, 354 students participated (M[subscript age] = 19.76, SD[subscript age] = 2.93 years), 66% identifying as female. One instructor…

Five cases of caudal regression with an aberrant abdominal umbilical artery: Further support for a caudal regression-sirenomelia spectrum.

Science.gov (United States)

Duesterhoeft, Sara M; Ernst, Linda M; Siebert, Joseph R; Kapur, Raj P

2007-12-15

Sirenomelia and caudal regression have sparked centuries of interest and recent debate regarding their classification and pathogenetic relationship. Specific anomalies are common to both conditions, but aside from fusion of the lower extremities, an aberrant abdominal umbilical artery ("persistent vitelline artery") has been invoked as the chief anatomic finding that distinguishes sirenomelia from caudal regression. This observation is important from a pathogenetic viewpoint, in that diversion of blood away from the caudal portion of the embryo through the abdominal umbilical artery ("vascular steal") has been proposed as the primary mechanism leading to sirenomelia. In contrast, caudal regression is hypothesized to arise from primary deficiency of caudal mesoderm. We present five cases of caudal regression that exhibit an aberrant abdominal umbilical artery similar to that typically associated with sirenomelia. Review of the literature identified four similar cases. Collectively, the series lends support for a caudal regression-sirenomelia spectrum with a common pathogenetic basis and suggests that abnormal umbilical arterial anatomy may be the consequence, rather than the cause, of deficient caudal mesoderm. (c) 2007 Wiley-Liss, Inc.

Global Journal of Environmental Sciences - Vol 4, No 2 (2005)

African Journals Online (AJOL)

Open Access DOWNLOAD FULL TEXT Subscription or Fee Access ... Monitoring air pollutants due to gas flaring using rain water · EMAIL FREE FULL TEXT ... from an industrial chimney · EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia; Rue, Haavard

2018-01-01

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

CODESRIA Bulletin - Vol 1999, No 1-2 (1999)

African Journals Online (AJOL)

Subscription or Fee Access. Table of Contents. Articles. Negotiating Post-war Identities: Child Soldiers in Mozambique and Angola. Alcinda Honwana. Congolese Immigrants in South ... Currency and Sovereignty. Hakim Ben Hammouda. Men and Women Between the Public and Private Sphere. Penda Mbow. Book Review: ...

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Constrained statistical inference : sample-size tables for ANOVA and regression

NARCIS (Netherlands)

Vanbrabant, Leonard; Van De Schoot, Rens; Rosseel, Yves

2015-01-01

Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than β2 and β3. The corresponding hypothesis is H: β1 > {β2, β3} and

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

Science.gov (United States)

Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

2015-04-01

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.

Science.gov (United States)

Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R

2016-12-01

: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We

Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Directory of Open Access Journals (Sweden)

Weibo Zhao

2017-12-01

Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Science.gov (United States)

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Demonstration of a Fiber Optic Regression Probe

Science.gov (United States)

Korman, Valentin; Polzin, Kurt A.

2010-01-01

The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

Caudal regression syndrome : a case report

International Nuclear Information System (INIS)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun

1998-01-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging

Caudal regression syndrome : a case report

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)

1998-07-01

Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.

Correlation and simple linear regression.

Science.gov (United States)

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

bayesQR: A Bayesian Approach to Quantile Regression

Directory of Open Access Journals (Sweden)

Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

Bone marrow endothelial progenitors augment atherosclerotic plaque regression in a mouse model of plasma lipid lowering

Science.gov (United States)

Yao, Longbiao; Heuser-Baker, Janet; Herlea-Pana, Oana; Iida, Ryuji; Wang, Qilong; Zou, Ming-Hui; Barlic-Dicen, Jana

2012-01-01

The major event initiating atherosclerosis is hypercholesterolemia-induced disruption of vascular endothelium integrity. In settings of endothelial damage, endothelial progenitor cells (EPCs) are mobilized from bone marrow into circulation and home to sites of vascular injury where they aid endothelial regeneration. Given the beneficial effects of EPCs in vascular repair, we hypothesized that these cells play a pivotal role in atherosclerosis regression. We tested our hypothesis in the atherosclerosis-prone mouse model in which hypercholesterolemia, one of the main factors affecting EPC homeostasis, is reversible (Reversa mice). In these mice normalization of plasma lipids decreased atherosclerotic burden; however, plaque regression was incomplete. To explore whether endothelial progenitors contribute to atherosclerosis regression, bone marrow EPCs from a transgenic strain expressing green fluorescent protein under the control of endothelial cell-specific Tie2 promoter (Tie2-GFP+) were isolated. These cells were then adoptively transferred into atheroregressing Reversa recipients where they augmented plaque regression induced by reversal of hypercholesterolemia. Advanced plaque regression correlated with engraftment of Tie2-GFP+ EPCs into endothelium and resulted in an increase in atheroprotective nitric oxide and improved vascular relaxation. Similarly augmented plaque regression was also detected in regressing Reversa mice treated with the stem cell mobilizer AMD3100 which also mobilizes EPCs to peripheral blood. We conclude that correction of hypercholesterolemia in Reversa mice leads to partial plaque regression that can be augmented by AMD3100 treatment or by adoptive transfer of EPCs. This suggests that direct cell therapy or indirect progenitor cell mobilization therapy may be used in combination with statins to treat atherosclerosis. PMID:23081735

The Validity of a German Version of the Career Exploration Survey

Science.gov (United States)

Rowold, Jens; Staufenbiel, Kathrin

2010-01-01

This paper reports psychometric properties of a German version of the Career Exploration Survey (CES-G). The instrument's 16 scales allow for a detailed description of career exploration. Based on data from two studies (N[subscript 1] = 1023; N[subscript 2] = 816), confirmatory factor analyses supported the 16-factor model. With regard to…

Effects of Web-Based Interactive Modules on Engineering Students' Learning Motivations

Science.gov (United States)

Bai, Haiyan; Aman, Amjad; Xu, Yunjun; Orlovskaya, Nina; Zhou, Mingming

2016-01-01

The purpose of this study is to assess the impact of a newly developed modules, Interactive Web-Based Visualization Tools for Gluing Undergraduate Fuel Cell Systems Courses system (IGLU), on learning motivations of engineering students using two samples (n[subscript 1] = 144 and n[subscript 2] = 135) from senior engineering classes. The…

Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

Science.gov (United States)

Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

2018-03-01

It is of great significance to design glasses with satisfactory mechanical properties predictively through modeling. Among various modeling methods, data-driven modeling is such a reliable approach that can dramatically shorten research duration, cut research cost and accelerate the development of glass materials. In this work, the ridge regression (RR) analysis was used to construct regression models for predicting the compositional dependence of CaO-Al2O3-SiO2 glass elastic moduli (Shear, Bulk, and Young’s moduli) and hardness based on the ternary diagram of the compositions. The property prediction over a large glass composition space was accomplished with known experimental data of various compositions in the literature, and the simulated results are in good agreement with the measured ones. This regression model can serve as a facile and effective tool for studying the relationship between the compositions and the property, enabling high-efficient design of glasses to meet the requirements for specific elasticity and hardness.

Regression Techniques for Determining the Effective Impervious Area in Southern California Watersheds

Science.gov (United States)

Sultana, R.; Mroczek, M.; Dallman, S.; Sengupta, A.; Stein, E. D.

2016-12-01

The portion of the Total Impervious Area (TIA) that is hydraulically connected to the storm drainage network is called the Effective Impervious Area (EIA). The remaining fraction of impervious area, called the non-effective impervious area, drains onto pervious surfaces which do not contribute to runoff for smaller events. Using the TIA instead of EIA in models and calculations can lead to overestimates of runoff volumes peak discharges and oversizing of drainage system since it is assumed all impervious areas produce urban runoff that is directly connected to storm drains. This makes EIA a better predictor of actual runoff from urban catchments for hydraulic design of storm drain systems and modeling non-point source pollution. Compared to TIA, determining the EIA is considerably more difficult to calculate since it cannot be found by using remote sensing techniques, readily available EIA datasets, or aerial imagery interpretation alone. For this study, EIA percentages were calculated by two successive regression methods for five watersheds (with areas of 8.38 - 158mi2) located in Southern California using rainfall-runoff event data for the years 2004 - 2007. Runoff generated from the smaller storm events are considered to be emanating only from the effective impervious areas. Therefore, larger events that were considered to have runoff from both impervious and pervious surfaces were successively removed in the regression methods using a criterion of (1) 1mm and (2) a max (2 , 1mm) above the regression line. MSE is calculated from actual runoff and runoff predicted by the regression. Analysis of standard deviations showed that criterion of max (2 , 1mm) better fit the regression line and is the preferred method in predicting the EIA percentage. The estimated EIAs have shown to be approximately 78% to 43% of the TIA which shows use of EIA instead of TIA can have significant impact on the cost building urban hydraulic systems and stormwater capture devices.

QSAR Modeling of COX -2 Inhibitory Activity of Some Dihydropyridine and Hydroquinoline Derivatives Using Multiple Linear Regression (MLR) Method.

Science.gov (United States)

Akbari, Somaye; Zebardast, Tannaz; Zarghi, Afshin; Hajimahdi, Zahra

2017-01-01

COX-2 inhibitory activities of some 1,4-dihydropyridine and 5-oxo-1,4,5,6,7,8-hexahydroquinoline derivatives were modeled by quantitative structure-activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R 2 ) of 0.972 and 0.531 for training and test groups, respectively. The quality of the model was evaluated by leave-one-out (LOO) cross validation (LOO correlation coefficient (Q 2 ) of 0.943) and Y-randomization. We also employed a leverage approach for the defining of applicability domain of model. Based on QSAR models results, COX-2 inhibitory activity of selected data set had correlation with BEHm6 (highest eigenvalue n. 6 of Burden matrix/weighted by atomic masses), Mor03u (signal 03/unweighted) and IVDE (Mean information content on the vertex degree equality) descriptors which derived from their structures.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Science.gov (United States)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

Science.gov (United States)

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Acknow ledgemen ts -9-2---------------------------~---------------~--O-NA ...

Indian Academy of Sciences (India)

Sendyour subscriptions by DD or MO infavour of. "Indian Academy of Sciences" to Circulation Department, Indian Academy of Sciences,. C V Raman Avenue, P B No. 8005, Bangalore 560080, India. Edited and published by V K Gaur for the Indian Academy of Sciences,. Bangalore 560080. Printed at Thomson Press (J) Ltd.

Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

Directory of Open Access Journals (Sweden)

Yi-Ming Kuo

2011-06-01

Full Text Available Fine airborne particulate matter (PM2.5 has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS, the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME method. The resulting epistemic framework can assimilate knowledge bases including: (a empirical-based spatial trends of PM concentration based on landuse regression, (b the spatio-temporal dependence among PM observation information, and (c site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan from 2005–2007.

Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods.

Science.gov (United States)

Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

2011-06-01

Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

Regressing Multiple Viral Plaques and Skin Fragility Syndrome in a Cat Coinfected with FcaPV2 and FcaPV3

Directory of Open Access Journals (Sweden)

Alberto Alberti

2015-01-01

Full Text Available Feline viral plaques are uncommon skin lesions clinically characterized by multiple, often pigmented, and slightly raised lesions. Numerous reports suggest that papillomaviruses (PVs are involved in their development. Immunosuppressed and immunocompetent cats are both affected, the biological behavior is variable, and the regression is possible but rarely documented. Here we report a case of a FIV-positive cat with skin fragility syndrome and regressing multiple viral plaques in which the contemporary presence of two PV types (FcaPV2 and FcaPV3 was demonstrated by combining a quantitative molecular approach to histopathology. The cat, under glucocorticoid therapy for stomatitis and pruritus, developed skin fragility and numerous grouped slightly raised nonulcerated pigmented macules and plaques with histological features of epidermal thickness, mild dysplasia, and presence of koilocytes. Absolute quantification of the viral DNA copies (4555 copies/microliter of FcaPV2 and 8655 copies/microliter of FcaPV3 was obtained. Eighteen months after discontinuation of glucocorticoid therapy skin fragility and viral plaques had resolved. The role of the two viruses cannot be established and it remains undetermined how each of the viruses has contributed to the onset of VP; the spontaneous remission of skin lesions might have been induced by FIV status change over time due to glucocorticoid withdraw and by glucocorticoids withdraw itself.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

Quasi-experimental evidence on tobacco tax regressivity.

Science.gov (United States)

Koch, Steven F

2018-01-01

Tobacco taxes are known to reduce tobacco consumption and to be regressive, such that tobacco control policy may have the perverse effect of further harming the poor. However, if tobacco consumption falls faster amongst the poor than the rich, tobacco control policy can actually be progressive. We take advantage of persistent and committed tobacco control activities in South Africa to examine the household tobacco expenditure burden. For the analysis, we make use of two South African Income and Expenditure Surveys (2005/06 and 2010/11) that span a series of such tax increases and have been matched across the years, yielding 7806 matched pairs of tobacco consuming households and 4909 matched pairs of cigarette consuming households. By matching households across the surveys, we are able to examine both the regressivity of the household tobacco burden, and any change in that regressivity, and since tobacco taxes have been a consistent component of tobacco prices, our results also relate to the regressivity of tobacco taxes. Like previous research into cigarette and tobacco expenditures, we find that the tobacco burden is regressive; thus, so are tobacco taxes. However, we find that over the five-year period considered, the tobacco burden has decreased, and, most importantly, falls less heavily on the poor. Thus, the tobacco burden and the tobacco tax is less regressive in 2010/11 than in 2005/06. Thus, increased tobacco taxes can, in at least some circumstances, reduce the financial burden that tobacco places on households. Copyright © 2017 Elsevier Ltd. All rights reserved.

Polylinear regression analysis in radiochemistry

International Nuclear Information System (INIS)

Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

1995-01-01

A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

Relationship of oestrus synchronization method, circulating hormones, luteinizing hormone and prostaglandin F-2 alpha receptors and luteal progesterone concentration to premature luteal regression in superovulated sheep.

Science.gov (United States)

Schiewe, M C; Fitz, T A; Brown, J L; Stuart, L D; Wildt, D E

1991-09-01

Ewes were treated with exogenous follicle-stimulating hormone (FSH) and oestrus was synchronized using either a dual prostaglandin F-2 alpha (PGF-2 alpha) injection regimen or pessaries impregnated with medroxy progesterone acetate (MAP). Natural cycling ewes served as controls. After oestrus or AI (Day 0), corpora lutea (CL) were enucleated surgically from the left and right ovaries on Days 3 and 6, respectively. The incidence of premature luteolysis was related (P less than 0.05) to PGF-2 alpha treatment and occurred in 7 of 8 ewes compared with 0 of 4 controls and 1 of 8 MAP-exposed females. Sheep with regressing CL had lower circulating and intraluteal progesterone concentrations and fewer total and small dissociated luteal cells on Day 3 than gonadotrophin-treated counterparts with normal CL. Progesterone concentration in the serum and luteal tissue was higher (P less than 0.05) in gonadotrophin-treated ewes with normal CL than in the controls; but luteinizing hormone (LH) receptors/cell were not different on Days 3 and 6. There were no apparent differences in the temporal patterns of circulating oestradiol-17 beta, FSH and LH. High progesterone in gonadotrophin-treated ewes with normal CL coincided with an increase in total luteal mass and numbers of cells, which were primarily reflected in more small luteal cells than in control ewes. Gonadotrophin-treated ewes with regressing CL on Day 3 tended (P less than 0.10) to have fewer small luteal cells and fewer (P less than 0.05) low-affinity PGF-2 alpha binding sites than sheep with normal CL. By Day 6, luteal integrity and cell viability was absent in ewes with prematurely regressed CL. These data demonstrate that (i) the incidence of premature luteal regression is highly correlated with the use of PGF-2 alpha; (ii) this abnormal luteal tissue is functionally competent for 2-3 days after ovulation, but deteriorates rapidly thereafter and (iii) luteal-dysfunctioning ewes experience a reduction in numbers of

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Variable and subset selection in PLS regression

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2001-01-01

The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

Ridge Regression Signal Processing

Science.gov (United States)

Kuhl, Mark R.

1990-01-01

The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

BRGLM, Interactive Linear Regression Analysis by Least Square Fit

International Nuclear Information System (INIS)

Ringland, J.T.; Bohrer, R.E.; Sherman, M.E.

1985-01-01

1 - Description of program or function: BRGLM is an interactive program written to fit general linear regression models by least squares and to provide a variety of statistical diagnostic information about the fit. Stepwise and all-subsets regression can be carried out also. There are facilities for interactive data management (e.g. setting missing value flags, data transformations) and tools for constructing design matrices for the more commonly-used models such as factorials, cubic Splines, and auto-regressions. 2 - Method of solution: The least squares computations are based on the orthogonal (QR) decomposition of the design matrix obtained using the modified Gram-Schmidt algorithm. 3 - Restrictions on the complexity of the problem: The current release of BRGLM allows maxima of 1000 observations, 99 variables, and 3000 words of main memory workspace. For a problem with N observations and P variables, the number of words of main memory storage required is MAX(N*(P+6), N*P+P*P+3*N, and 3*P*P+6*N). Any linear model may be fit although the in-memory workspace will have to be increased for larger problems

Volcano Plot for Bimetallic Catalysts in Hydrogen Generation by Hydrolysis of Sodium Borohydride

Science.gov (United States)

Koska, Anais; Toshikj, Nikola; Hoett, Sandra; Bernaud, Laurent; Demirci, Umit B.

2017-01-01

In the field of "hydrogen energy", sodium borohydride (NaBH[subscript 4]) is a potential hydrogen carrier able to release H[subscript 2] by hydrolysis in the presence of a metal catalyst. Our laboratory experiment focuses on this. It is intended for thirdyear undergraduate students in order to have hands-on laboratory experience through…

Sex Comparisons for Relative Peak Torque and Electromyographic Mean Frequency during Fatigue

Science.gov (United States)

Stock, Matt S.; Beck, Travis W.; DeFreitas, Jason M.; Ye, Xin

2013-01-01

Purpose: This study compared the relative peak torque and normalized electromyographic (EMG) mean frequency (MNF) responses during fatiguing isokinetic muscle actions for men versus women. Method: Twenty men M[subscript age] ± SD = 22 ± 2 years) and 20 women M[subscript age] ± SD = 22 ± 1 years) performed 50 maximal concentric isokinetic muscle…

Laboratory Experiments on the Electrochemical Remediation of the Environment. Part 9: Microscale Recovery of a Soil Metal Pollutant and Its Extractant

Science.gov (United States)

Ibanez, Jorge G.; Balderas-Hernandez, Patricia; Garcia-Pintor, Elizabeth; Barba-Gonzalez, Sandy Nohemi; Doria-Serrano, Ma. del Carmen; Hernaiz-Arce, Lorena; Diaz-Perez, Armando; Lozano-Cusi, Ana

2011-01-01

Many soils throughout the world are contaminated with metal salts of diverse toxicity. We have developed an experiment to demonstrate the removal of a metal from an insoluble surrogate soil pollutant, CuCO[subscript 3] multiplied by Cu(OH)[subscript 2], by complexation followed by the simultaneous electrochemical recovery of the ligand (i.e.,…

Determination of Carbon Dioxide, Carbon Monoxide, and Methane Concentrations in Cigarette Smoke by Fourier Transform Infrared Spectroscopy

Science.gov (United States)

Tan, T. L.; Lebron, G. B.

2012-01-01

The integrated absorbance areas of vibrational bands of CO[subscript 2], CO, and CH[subscript 4] gases in cigarette smoke were measured from Fourier transform infrared (FTIR) spectra to derive the partial pressures of these gases at different smoke times. The quantity of the three gas-phase components of cigarette smoke at different smoke times…

Regression filter for signal resolution

International Nuclear Information System (INIS)

Matthes, W.

1975-01-01

The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)

Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?

Science.gov (United States)

Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.

2016-12-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.

Medición de la Calidad del Servicio para Agentes de Suscripción en Revistas Digitales a través del Modelo Servqual (Measuring the Quality of Service for Subscription Agents in eJournals through Model Servqual

Directory of Open Access Journals (Sweden)

Salvador Enrique Vazquez Moctezuma

2015-07-01

Full Text Available Resumen Las bibliotecas, hospitales, bancos y centros de investigación compran revistas académicas en formato digital con editores e intermediarios para proveerlas a los usuarios que las necesitan. Los administradores con frecuencia califican los servicios que ofertan hacia sus usuarios, pero se olvidan en evaluar a los agentes de suscripción, los cuales a su vez repercuten en el servicio que se brinda a los usuarios finales. Por otra parte, los agentes requieren conocer la percepción que tienen sus clientes/bibliotecas al utilizar sus servicios con el fin de rediseñar, incrementar o eliminar alguno de estos, sin embargo tratar de medir la calidad de un servicio es complejo, puesto que son acciones donde los resultados pueden verse o sentirse, pero la actividad en sí misma es intangible. El presente estudio tiene como propósito esencial, realizar un acercamiento para determinar las percepciones y expectativas que tienen 31 administradores de cinco países de Latinoamérica, que contrataron el servicio de gestión de accesos a través de agencias de suscripción y de la cual son los principales verificadores de accesos a revistas electrónicas. También se mencionan los servicios que ofrece el agente de suscripciones así como se describe el modelo Servqual. Con respecto a la metodología se aplicó un cuestionario con escala Likert. De forma general, se encontró que las expectativas de los clientes superan sus percepciones. Abstract Libraries, hospitals, banks and research centers buy academic journals in digital format with publishers and intermediaries to provide them to the users who need them. Administrators often qualify the services they offer to their users, but forget to rate subscription agents, which in turn affect the service to end users is provided. Moreover, agents need to know the perception your customers / libraries to use their services in order to redesign, increase or remove any of these, however try to measure the

A binary logistic regression model with complex sampling design of ...

African Journals Online (AJOL)

2017-09-03

Sep 3, 2017 ... Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted. .... Data was entered into STATA-12 and analyzed using. SPSS-21. .... lack of access/too far or costs too much. 35. 1.2.

Direction of Effects in Multiple Linear Regression Models.

Science.gov (United States)

Wiedermann, Wolfgang; von Eye, Alexander

2015-01-01

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

Effect of candesartan on progression and regression of retinopathy in type 2 diabetes (DIRECT-Protect 2): a randomised placebo-controlled trial

DEFF Research Database (Denmark)

Sjolie, A.K.; Klein, R.; Porta, M.

2008-01-01

-group, placebo-controlled trial in 309 centres worldwide. We recruited normoalbuminuric, normotensive, or treated hypertensive people with type 2 diabetes with mild to moderately severe retinopathy and assigned them to candesartan 16 mg once a day or placebo. After a month, the dose was doubled to 32 mg once per...... day. Investigators and patients were unaware of the treatment allocation status. Progression of retinopathy was the primary endpoint, and regression was a secondary endpoint. Analysis was by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00252694. FINDINGS: 1905...... or changes in blood pressure during the trial. An overall change towards less severe retinopathy by the end of the trial was observed in the candesartan group (odds 1.17, 95% CI 1.05-1.30, p=0.003). Adverse events did not differ between the treatment groups. INTERPRETATION: Treatment with candesartan in type...

Comparing spatial regression to random forests for large ...

Science.gov (United States)

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

A Simulation Investigation of Principal Component Regression.

Science.gov (United States)

Allen, David E.

Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

Hierarchical regression analysis in structural Equation Modeling

NARCIS (Netherlands)

de Jong, P.F.

1999-01-01

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main

Repeated Results Analysis for Middleware Regression Benchmarking

Czech Academy of Sciences Publication Activity Database

Bulej, Lubomír; Kalibera, T.; Tůma, P.

2005-01-01

Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

Regression and progression of microalbuminuria in adolescents with childhood onset diabetes mellitus

Directory of Open Access Journals (Sweden)

Mi Kyung Son

2015-03-01

Full Text Available PurposeAlthough microalbuminuria is considered as an early marker of nephropathy in diabetic adults, available information in diabetic adolescents is limited. The aim of this study was to investigate prevalence and frequency of regression of microalbuminuria in type 1 (T1DM and type 2 diabetes mellitus (T2DM patients with childhood onset.MethodsOne hundred and nine adolescents (median, 18.9 years; interquartile range (IQR, 16.5-21.0 years with T1DM and 18 T2DM adolescents (median, 17.9 years; IQR, 16.8-18.4 years with repeated measurements of microalbuminuria (first morning urine microalbumin/creatinine ratios were included. The median duration of diabetes was 10.1 (7.8-14.0 years and 5.0 (3.5-5.6 years, respectively, and follow-up period ranged 0.5-7.0 years. Growth parameters, estimated glomerular filtration rate, glycosylated hemoglobin (HbA1c and lipid profiles were obtained after reviewing medical record in each subject.ResultsThe prevalence of microalbuminuria at baseline and evaluation were 21.1% and 17.4% in T1DM, and 44.4% and 38.9% in T2DM. Regression of microalbuminuria was observed in 13 T1DM patients (56.5% and 3 T2DM patients (37.5%, and progression rate was 10.5% and 20% in T1DM and T2DM respectively. In regression T1DM group, HbA1c at baseline and follow-up was lower, and C-peptide at baseline was higher compared to persistent or progression groups. In T2DM, higher triglyceride was observed in persistent group.ConclusionConsiderable regression of microalbuminuria more than progression in diabetes adolescents indicates elevated urinary microalbumin excretion in a single test does not imply irreversible diabetic nephropathy. Careful monitoring and adequate intervention should be emphasized in adolescents with microalbuminuria to prevent rapid progression toward diabetic nephropathy.

Global Journal of Pure and Applied Sciences - Vol 13, No 2 (2007)

African Journals Online (AJOL)

Open Access DOWNLOAD FULL TEXT Subscription or Fee Access .... Measurements, analysis and impact of industrial noise on workers and community ... Frequency-dependent noise characteristics in a gas-to-liquid plant in the swamp area ...

and Multinomial Logistic Regression

African Journals Online (AJOL)

This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).

Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

Science.gov (United States)

Song, Chao; Kwan, Mei-Po; Zhu, Jiping

2017-04-08

An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

International Nuclear Information System (INIS)

Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

1985-01-01

Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation

Directory of Open Access Journals (Sweden)

Tomoya Mita

2017-01-01

Full Text Available Background. The effect of dipeptidyl peptidase-4 (DPP-4 inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT in insulin-treated patients with type 2 diabetes mellitus (T2DM. Methods. This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results. The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P = 0.022 and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P = 0.007, but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions. Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396.

Regression away from the mean: Theory and examples.

Science.gov (United States)

Schwarz, Wolf; Reike, Dennis

2018-02-01

Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs. © 2017 The British Psychological Society.

Bayesian logistic regression analysis

NARCIS (Netherlands)

Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

2012-01-01

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

Motional Mechanisms of Homopolar Motors & Rollers

Science.gov (United States)

Wong, H. K.

2009-01-01

The strong Nd[subscript 2]Fe[subscript 14]B permanent magnet has facilitated development of various fascinating yet simple homopolar motors. However, the physics of these devices is often not explained, or is explained incorrectly. A major concern is that Newton's third law was overlooked in some of the earlier articles. In this paper, I will…

Brief Report: Inter-Relationship between Emotion Regulation, Intolerance of Uncertainty, Anxiety, and Depression in Youth with Autism Spectrum Disorder

Science.gov (United States)

Cai, Ru Ying; Richdale, Amanda L.; Dissanayake, Cheryl; Uljarevic, Mirko

2018-01-01

The aim of this study was to examine the inter-relationship between emotion regulation (ER), intolerance of uncertainty (IU), and symptoms of anxiety and depression in adolescents and young adults diagnosed with autism spectrum disorder (ASD). Sixty-one individuals aged 14-24 years (M[subscript age] = 18.19; SD[subscript age] = 2.19) completed the…