Podcasts about regression analysis

Dave talks about WR regression analysis and applies it to the 2024 Rookie WR class... Subscribe to the RotoViz YouTube Channel here! HOSTS Dave Caban (@dcabanff) – RotoViz Co-Owner  Curtis Patrick (@CPatricknfl) – RotoViz Co-Owner  SPONSORS BetterHelp - This episode is brought to you by BetterHelp. Give online therapy a try at betterhelp.com/ROTOVIZ and get on your way to being your best self. Underdog Fantasy – Get a 100% deposit match on your first deposit up to $100 when you sign up at Underdogfantasy.com using this link or the promo code ROTOVIZ. Listeners of RotoViz Radio can save 10% on a one-year RotoViz subscription by visiting RotoViz.com/podcast or by using the promotional code "rvradio2024" at the time of purchase. Gametime - Download the Gametime app, create an account, and use code ROTOVIZ for $20 off your first purchase. SHOW NOTES Email: RotoVizFFshow@gmail.com  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Dave talks about WR regression analysis and applies it to the 2024 Rookie WR class... Subscribe to the RotoViz YouTube Channel here! HOSTS Dave Caban (@dcabanff) – RotoViz Co-Owner  Curtis Patrick (@CPatricknfl) – RotoViz Co-Owner  SPONSORS BetterHelp - This episode is brought to you by BetterHelp. Give online therapy a try at betterhelp.com/ROTOVIZ and get on your way to being your best self. Underdog Fantasy – Get a 100% deposit match on your first deposit up to $100 when you sign up at Underdogfantasy.com using this link or the promo code ROTOVIZ. Listeners of RotoViz Radio can save 10% on a one-year RotoViz subscription by visiting RotoViz.com/podcast or by using the promotional code "rvradio2024" at the time of purchase. Gametime - Download the Gametime app, create an account, and use code ROTOVIZ for $20 off your first purchase. SHOW NOTES Email: RotoVizFFshow@gmail.com  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Bonus track :-)Thank you for offers to donate to my political campaign, but frankly I OBJECT to the current state of campaign finance rules and until we #overturn #citizensunited. And allowing a flood of dark money from billionaire nut jobs who buy our Supreme Court justices :-( No thanks! But you're welcome to donate to my GoFundMe: gofund.me/10543a02 Especially if you dig my artwork, just let me know any donation of any amount even one penny I'm serious I appreciate one penny haha In thanks for even one penny donation

Dr. Wilfred Reilly—Professor of Political Science at Kentucky State University & Author of “Lies My Liberal Teacher Told Me”—joins The Rich Zeoli Show to discuss his latest piece at National Review, “The Intersectionality Canard.” Dr. Reilly writes: “'Intersectionality' is just a badly done ‘woke' version of regression analysis…The idea that multiple independent variables can influence a dependent variable like income is not exactly a new one. And, the actual range of potential ‘IVs' that can do so extends well beyond race and sex to include: age, the regions where people and groups live, test and IQ scores, patterns of study time, crime rates, desire to work at all (in the context of men vs. women), and so on down the line.” You can find the full editorial here: https://www.nationalreview.com/2024/01/the-intersectionality-canard/. And you can pre-order Dr. Reilly's upcoming book here: https://www.amazon.com/Lies-My-Liberal-Teacher-Told/dp/0063265974.

This episode is a compilation of answers to YOUR questions that were asked directly from my listeners who attend my weekly business education YouTube live webcast. Topics covered include: How to perform a regression analysis, Do you think your house is an asset, How to gain confidence and more. Refer to chapter marks for a complete list of topics covered and to jump to a specific section. Download my free "Networking eBook": www.harouneducation.comAttend my weekly YouTube Live every Thursday's 8am-11am PT. Subscribe to my YouTube Channel to receive notifications. Learn more about my MBA Degree ProgramConnect with me: YouTube: ChrisHarounVenturesCompleteBusinessEducationInstagram @chrisharounLinkedIn: Chris HarounTwitter: @chris_harounFacebook: Haroun Education Ventures  TikTok: @chrisharoun

Jordan Backes (@JordanBackes33) takes a look at trying to predict how the 2023 Rookie QB class will perform their first year in the NFL through multiple regression models - Decision Trees and Linear Regression. JB also takes a look at the year 2 model for the 2022 QBs through the same methods. Which method is superior, how to these signal callers look going forward and who are some buys and sells? All Gas Newsletter - https://allgas.beehiiv.com/ Patreon - https://Patreon.com/AllGas Learn more about your ad choices. Visit megaphone.fm/adchoices

Jordan Backes (@JordanBackes33) takes a look at trying to predict how the 2023 rookie WR class will perform their first year in the NFL through multiple regression models - Decision Trees and Linear Regression. JB also takes a look at the year 2 model for the 2022 WR class through the same methods. Which method is superior and how do these WRs project going forward? All Gas Newsletter - https://allgas.beehiiv.com/ Patreon - https://Patreon.com/AllGas Learn more about your ad choices. Visit megaphone.fm/adchoices

InfosecTrain hosts a live event entitled ‘Data Science Fast Track Course' with certified expert ‘NAWAJ'. Data Science is not the future anymore, it is rather the present. This masterclass would be extremely beneficial to anyone interested in pursuing a career in Data Science. It will be delivered by a domain expert with extensive industry experience. With our instructors who are specialists in their disciplines, we hold a global reputation. Attending this webinar will benefit you in a variety of ways. Thank you for Listening this Podcast, For more details or free demo with our expert write into us at sales@infosectrain.com ➡️ Agenda

Not sure what exam to take? Take the QUIZ: https://www.hrdefined.com/take-the-quiz WHERE TO FIND US: Web: www.hrdefined.com Email: info@hrdefined.com LinkedIn: linkedin/company/hrdefined Facebook: facebook.com/hrdefined Instagram: @hrdefined GRAB THE BOOTCAMP: https://learn.hrdefined.com/courses/exambootcamp Immediate Access Community Support 500+ Exam Questions 6 months access BONUS #1: Complex Concepts BONUS #2: Precedence Cases BONUS #3: Case Studies About Us: At HR Defined, we equip HR professionals with the tools they need to become thought leaders. Our instructors encourage students to deep dive into the HR principles and empower them to pursue their certification goals. HR Defined strives to bring perseverance and resiliency to human resources professionals while simultaneously helping them gain the confidence they need to ace their certification exams. About Cari Hawthorne: Lead Instructor at HR Defined in Houston, TX. Follow Cari Hawthorne: www.linkedin.com/in/carihawthorne1 Cari Hawthorne is an Army Veteran, Author and Certified Human Resources Professional. Her strength lies in her ability to provide HR professionals with the tools they need to become thought leaders. Cari works diligently to equip human resources professionals with the knowledge and confidence they need to ace their certification exams and excel in their careers. Her background spans over the last 14 years specializing in career coaching, talent management, recruiting and training and development. Currently she is a Senior Global Human Manager for a Fortune 500 company where advises senior leaders on people and strategy. #humanresources #shrm #hrcertification #hrdefined

https://psychiatry.dev/wp-content/uploads/speaker/post-11286.mp3?cb=1671776092.mp3 Playback speed: 0.8x 1x 1.3x 1.6x 2x Download: Relationship between obsessive compulsive symptomatology and severity of psychotic symptoms in schizophrenia: Meta-analysis and meta-regression analysis – Review R Cunill et al.Full EntryRelationship between obsessive compulsive symptomatology and severity of psychotic symptoms in schizophrenia: Meta-analysis and meta-regression analysis –

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.14.516465v1?rss=1 Authors: Pisani, S., Gunasekera, B., Lu, Y., Vignando, M., ffytche, D., Aarsland, D., Chaudhuri, K. R., Ballard, C., Lee, J.-Y., Kim, Y. K., Velayudhan, L., Bhattacharyya, S. Abstract: Background: Common neural underpinning of Parkinson's Disease (PD) psychosis across different structural magnetic resonance imaging (MRI) studies remains unclear to this day with few studies and even fewer meta-analyses available. Objectives: Our meta-analysis aimed to identify and summarise studies using MRI approach to identify PD psychosis-specific brain regions and examine the relation between cortical volume loss and dopaminergic and serotonergic receptor density. Methods: PubMed, Web of Science and Embase were searched for MRI studies of PD psychosis (PDP) compared to PD patients without psychosis (PDnP). Seed-based d Mapping with Permutation of Subject Images was applied in the meta-analysis where coordinates were available. Multiple linear regressions to examine the relationship between grey matter volume loss in PDP and receptor gene expression density (extracted from the Allen Human Brain Atlas) were conducted in R. Results: We observed lower grey matter volume in parietal-temporo-occipital regions from our meta-analysis (N studies =10, PDP n=211, PDnP, n=298). These results remained significant after adjusting for PD medications and for cognitive scores. Grey matter volume loss in PDP was associated with local expression of 5-HT1a (b=0.109, p=0.012) and 5-HT2a receptors (b=-0.106, p=0.002) also after adjusting for PD medications (5-HT1a, p = 0.005; 5-HT2a, p = 0.001). Conclusions: Widespread cortical volume loss in the parieto-temporo-occipital regions involved in information processing and integration, as well as attention, could result in PD psychosis symptoms. Neurobiological mechanisms implicating serotonergic receptors may also contribute to this condition. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

You may have heard me say there is no magic formula for value pricing.That's true. There isn't.Value pricing is without a doubt the best way to price your services – but it takes time to implement, especially when you're stuck up on using hourly rates. But, we CAN break it down into baby steps. And the first step towards value pricing is to give a fixed price up front.Now for that… there IS a magic formula. It's called Regression Analysis. It's a way of using your current time-based pricing model to generate a fixed price upfront that will ensure you a profit every time.  All you need is a spreadsheet.Ready to get started?The latest episode of the Value Pricing Podcast is now available: Calculating a Pricing Formula Using Regression Analysis In today's episode you will learn:  ✅ Why value pricing is hard (but the best pricing method out there)✅ How to systematically calculate a profitable fixed price every time✅ How to systematically adjust your prices to cover scope ✅ How to make the first step towards value pricing Once you can confidently generate a fixed price up front, value pricing becomes FAR easier. Fixed pricing is not value pricing, but it's the first step on the journey towards it. 

In this segment, Jim and Scott  talk with Steve Malashevitz, DAU's Learning Asset Manager for CON 7410V - the Regression Analysis Course.  You will learn what the course is all about, what is covered, how it is structured, the target audience, and how students are assessed. These links are recommended for further information: I-Catalog for more info and to sign up: https://icatalog.dau.edu/onlinecatalog/courses.aspx?crs_id=12561 Credentials home page: https://www.dau.edu/training/pages/credentials.aspxBack to Basics: https://www.dau.edu/back-to-basics; BtB Contracting: https://www.dau.edu/functional-areas/contractingContracting Community of Practice: https://www.dau.edu/cop/contracting/Pages/Default.aspxIf you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA

What you will learn about in this episode:·        Statistical control processes·        Regression analysis·        JIT inventory management·        5S methodology·        Angel investing·        PhilanthropyAjay is a Sr. engineering manager at Medtronic, holds a master's degree in mechanical engineering from Arizona State University and an MBA from the University of California, Irvine. Ajay has a broad range of experience from working as a design engineer to cofounding a company to angel investing to philanthropy to leading engineering programs.Aaron Moncur, HostWe hope you enjoyed this episode of the Being an Engineer Podcast.Help us rank as the #1 engineering podcast on Apple and Spotify by leaving a review for us.You can find us under the category: mechanical engineering podcast on Apple Podcasts.Being an Engineer podcast is a go-to resource and podcast for engineering students on Spotify, too.Aaron Moncur and Rafael Testai love hearing from their listeners, so feel free to email us, connect on Facebook, Twitter, Instagram, and subscribe on Apple Podcast and Spotify! ABOUT BEING AN ENGINEERThe Being an Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community.The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us***Valued listener, we need your help getting to 100 podcast reviews. Win a $50 Amazon Gift card if you leave us a review on the Apple Podcasts. Simply email a screenshot of your 5-star review to Podcast@teampipeline.us , the email will be in the show notes. We will announce 5 lucky winners at the end of the first quarter in 2022.

An article from the Purposeful Relations blog on how public relations and communications need to interpret data and can use regression analysis to find a correlation between PR and comms and business impact.

In our first technically-focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct a common statistical technique called linear regression. They focus on how regression can be used to better understand the relations between key drivers of important outcomes. In this episode, they had conversations around these questions:  What is linear regression?  How are regression analyses used in organizational contexts?  How can linear regression be used to drive optimal business decisions? What are some steps an organization can take to more effectively utilize linear regression models?  Key Takeaways:  Linear Regression is a technique used to model relations between variables of interest and to use these relations to forecast future states. For example, in a simple linear regression, we might be interested in predicting a key outcome variable such as sales from other predictor variables such as number of customers. This kind of statistical technique can be used when the underlying relation between the predictor and outcome is linear (I.e., when the predictor and outcome is plotted, it follows a relatively straight line).  At the end of the episode, Jennifer and Ron recommend steps for folks just starting out in this space all the way to the more advanced HR professional.  Related Links  Millan Chicago 

As Filipino migrants, we've all heard assumptions about what our life is like abroad. These misconceptions have implications and may often lead to discord among friends or family members. In this episode, we tackled the true and often hard facts about living abroad. Thanks to the help of a group of fellow Filipinos from New York who gave their opinions and experiences, we came up with lists to help debunk these assumptions. If you enjoyed this episode, make sure and give us a five star rating  and leave us a review on iTunes, Podcast Addict, Podchaser and Castbox.  Follow us on Social Media:Taralets Talk Podcast on IGTaralets Talk Podcast on Facebook Taralets Talk would love to promote a Filipino charity. Message us any suggestions at charity@taraletstalk.com. Taralets Talk is sponsored by Disenyo.co LLC:DISCLAIMER: The opinions, beliefs, and viewpoints expressed by the hosts and guests on this podcast do not necessarily represent or reflect the official policy, opinions, beliefs, and viewpoints of Disenyo.co LLC and its employees. 

We use multiple regression analysis to determine the relation between many (multiple) independent variables and one single dependent variable. All slides to the entire series can be downloaded for free here: https://armintrost.de/en/professor/digital/social-research-methods/

"Michelle has somebody just stolen my car parking spot?" Mikey D and Coperate Steve take Bertie's spot and try to live up to the legend.

Ryan Bishop graduated with Full-time Web Development Cohort 36. I learned about software development working previously in a customer-facing technical role in the food production industry. This position introduced me to Statistical Data Analysis, Regression Analysis, Machine Learning, and programming and database management (Python/SQL). Leveraging this technical experience and a decade of business experience, I transitioned into software development via Nashville Software School. I'm a lifelong learner and inspired by continuous improvement principles, software design patterns, and passionate people. Let's solve problems together!

Dr. Jerz's lecture on correlation and linear regression.Transcript (srt) file: Correlation_and_Regression_Analysis

Shon Gerber from ShonGerber.com provides you the information and knowledge you need to prepare and pass the CISSP Exam while providing the tools you need to enhance your cybersecurity career.  Shon utilizes his expansive knowledge while providing superior training from his years of training people in cybersecurity.  In this episode, Shon will talk about questions for Domain 6 (Security Assessment and Testing) of the CISSP Exam: BTW - Get access to all my CISSP Training Courses here at:  https://shongerber.com/ Want to find Shon Gerber elsewhere on the internet? LinkedIn – www.linkedin.com/in/shongerber Facebook - https://www.facebook.com/CyberRiskReduced/ LINKS:  ISC2 Training Study Guide https://www.isc2.org/Training/Self-Study-Resources TechTarget https://searchsecurity.techtarget.com/quiz/CISSP-Domain-5-quiz-Types-of-access-control-systems?q0=0&x=77&y=10

6 Sigma อีกหนึ่งแนวคิดระดับโลกที่พัฒนาองค์กร ต่อเนื่องจาก EP ที่แล้ว สำหรับEP 57 นี้ มารู้จัก กับอีก 3 ขั้นตอนที่เหลือของ D M A I C ในการใช้ 6 Sigma กันนะ 3. Analyze ขั้นตอนนี้คือการวิเคราะห์สาเหตุของปัญหาหลัก ซึ่งเป็นการวิเคราะห์ในเชิงสถิติเพื่อระบุสาเหตุหลักที่ส่งผลโดยตรงต่อปัญหานั้น มี 3 ขั้นตอน a. การวิเคราะห์ข้อมูลตามลักษณะข้อมูลที่เก็บได้ ว่าเป็นการนับ หรือการวัด เช่น ผังการกระจาย (Scatter Diagram) ฮีสโตแกรม (Histogram) b. การวิเคราะห์กระบวนการ เพื่อหาขั้นตอนที่ไม่ก่อให้เกิดมูลค่าเพิ่ม สามารถนำแนวคิด Lean มาช่วยได้ จึงเรียกว่า Lean Six Sigma c. การวิเคราะห์หาสาเหตุที่แท้จริง ซึ่งเรียกสาเหตุหลักนี้ว่า KPIV (Key Process Input Variable) ซึ่งต้องสามารถระบุให้ชัดเจนว่า อะไรคือ KPIV ของปัญหาและต้องสามารถเชื่อมโยงกับ ตัวหลักของกระบวนการ หรือที่เรียกว่า KPOV (Key Process Output Variable) ให้ได้ หลักการสถิติที่ใช้ในการวิเคราะห์ ได้แก่ การตรวจสอบสมมติฐาน (Hypothesis Testing) ผังการกระจาย (Scattering Diagram) การวิเคราะห์การถดถอย (Regression Analysis) เป็นต้น 4. Improvement  a. สร้างแนวทางแก้ไขปัญหา ขั้นตอนนี้คือการปรับตั้งค่าสาเหตุหลัก (KPIV) โดยมีจุดประสงค์เพื่อให้ผลลัพธ์ของกระบวนการเป็นไปตามต้องการ ด้วยการใช้เทคนิคการออกแบบทดลอง(Design of Experiment : DOE) เพื่อปรับตั้งค่าสภาวะต่างๆของกระบวนการให้เป็นไปตามความต้องการ b. เลือกแนวทางการแก้ไขปัญหา 5. Control ขั้นตอนนี้เป็นขั้นตอนสุดท้าย ซึ่งต้องดำเนินการออกแบบระบบควบคุณคุณภาพของกระบวนการเพื่อให้เกิดความมั่นใจว่ากระบวนการจะย้อนไปมีปัญหาเหมือนเดิมอีก DMAIC a. กำหนดการวิธีการทางเทคนิคของการควบคุม เช่น วิธีในการสื่อสาร การผลิต ควบคุมการผลิต/บริการ จุดวิกฤติที่ต้องระมัดระวังในการทำงาน การใช้เครื่องมือสถิติ เช่น Control Chart เป็นต้น b. การจัดทำแผนตอบสนอง มาตรวัดที่สำคัญที่สุดสำหรับกระบวนการใหม่ ข้อกำหนดเฉพาะ และเป้าหมายใหม่ ผลการทำงานที่ได้รับการบันทึกใหม่ ซึ่งจะมีการติดตามอย่างต่อเนื่อง หวังว่าจะเป็นอีกหนึ่งแนวคิดในการบริหารงานของท่านให้ประสบความสำเร็จ หากท่านมีข้อแนะนำ หรือคำถามก็แจ้งเข้ามาใน Podbean ช่อง CM SMART INNOVATION ได้นะครับ และถ้ามีประโยชน์ขอช่วย Follow และแชร์ต่อๆไปด้วยครับ

Join Eric Forister as he discusses best practices when working with an expert using regression analysis.

Credit title: Subject Matter Expert : 1. Adi Teguh Suprapto, S.E., MM 2. Mulyono, S.E., M.M. Dokumenter: Binus University Uploaded by: Knowledge Management and Innovation Binus University

Pinball: We set up the final round of the 2017 Modern Widebody Tournament. We talk about Alien Pinball gameplay. And finally Dennis sets off to bore everyone with a regression analysis of Pinside's solid-state game ratings. Video Games: We of course cover Overwatch, both ongoing PTR tests and the new character (Orisa). Dennis hits on some 2017 GDC (Game Developers Conference) highlights. Tony tries to resist diving too deep into Aurora 4X (aka Spreadsheets in Space... ace... ce... e...). Tabletop: Tony talks a bit about International Tabletop Day. Links: 2017 Modern Widebody Tournament-Final Round Voting: https://goo.gl/forms/laVwA51PnaGbu8L03 The Link Cable Podcast (Episode 15 - TLOZBOTWOMG): https://www.podbean.com/media/share/pb-ggqss-6864ee Space Gambler (Playmatic 1978) Gameplay (no commentary): https://youtu.be/Q8Xe2nJMTT8 Pinside Thread on Regression Analysis of Pinside SS Ratings: https://pinside.com/pinball/forum/topic/regression-analysis-pinside-rating-and-game-details#post-3642419 Aurora 4X: http://aurora2.pentarch.org/index.php/topic,5663.0.html Quill18 plays Aurora 4X: https://www.youtube.com/watch?v=zcWln8Ne1RA&list=PLs3acGYgI1-sRFRB4BPB-FbmVuvwIKH92

A two-minute guide to one of the most important types of data analysis.

In this podcast, Italo Linfante, Miami Cardiac and Vascular Institute and Neuroscience Center, Baptist Hospital, Miami, USA, tells JNIS’ Editor Robert Tarr, the details of his study “Predictors of poor outcome despite recanalization: a multiple regression analysis of the NASA registry”. To access the full article, click here: http://goo.gl/wNEljN .

RICHARD NISBETT (https://www.edge.org/memberbio/richard_nisbett) is a professor of psychology and co-director of the Culture and Cognition Program at the University of Michigan. He is the author of Mindware: Tools for Smart Thinking; and The Geography of Thought. The Conversation: https://www.edge.org/conversation/richard_nisbett-the-crusade-against-multiple-regression-analysis

This lecture introduces the mathematical and statistical foundations of regression analysis, particularly linear regression.

Today we are joined by an appraiser out of Colorado who understands regression and statistical analysis.  If you have been wondering about this type of thing, Josh was wonderful to listen to and learn from.  

Statistical methods usually require that the analyzed data are correct and precise observations of the variables of interest. In practice, however, often only incomplete or uncertain information about the quantities of interest is available. The question studied in the present thesis is, how a regression analysis can reasonably be performed when the variables are only imprecisely observed. At first, different approaches to analyzing imprecisely observed variables that were proposed in the Statistics literature are discussed. Then, a new likelihood-based methodology for regression analysis with imprecise data called Likelihood-based Imprecise Regression is introduced. The corresponding methodological framework is very broad and permits accounting for coarsening errors, in contrast to most alternative approaches to analyzing imprecise data. The methodology suggests considering as the result of a regression analysis the entire set of all regression functions that cannot be excluded in the light of the data, which can be interpreted as a confidence set. In the subsequent chapter, a very general regression method is derived from the likelihood-based methodology. This regression method does not impose restrictive assumptions about the form of the imprecise observations, about the underlying probability distribution, and about the shape of the relationship between the variables. Moreover, an exact algorithm is developed for the special case of simple linear regression with interval data and selected statistical properties of this regression method are studied. The proposed regression method turns out to be robust in terms of a high breakdown point and to provide very reliable insights in the sense of a set-valued result with a high coverage probability. In addition, an alternative approach proposed in the literature based on Support Vector Regression is studied in detail and generalized by embedding it into the framework of the formerly introduced likelihood-based methodology. In the end, the discussed regression methods are applied to two practical questions.

Mike Brewer gives an introduction to regression discontinuity approach, which is a way of undertaking causal inference, usually of some policy intervention.

Barbara Sianesi gives an introduction to propensity score matching, which is a method/device to make two groups look the same.

Comparative Education, Critique of Neo-Classical Economics, Critical Pedagogy, Regression Analysis

NJIT

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or product positioning. This paper presents the results of an extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context with normal data. Unlike with previous studies, the performance is evaluated at a broad range of sample/segment size combinations being the most critical factors for the effectiveness of the criteria from both a theoretical and practical point of view. In order to assess the absolute performance of each criterion with respect to chance, the performance is reviewed against so called chance criteria, derived from discriminant analysis. The results induce recommendations on criterion selection when a certain sample size is given and help to judge what sample size is needed in order to guarantee an accurate decision based on a certain criterion respectively.

We present a unified semiparametric Bayesian approach based on Markov random field priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial effects as well as nonlinear effects of metrical covariates in flexible semiparametric form. Trend and seasonal components, different types of covariates and spatial effects are all treated within the same general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is fully Bayesian and uses MCMC techniques for posterior analysis. We provide two approaches: The first one is based on direct evaluation of observation likelihoods. The second one is based on latent semiparametric utility models and is particularly useful for probit models. The methods are illustrated by applications to unemployment data and a forest damage survey.

In this paper the data of a forest health inventory are analysed. Since 1983 the degree of defoliation (damage), together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions on the one side and the set of covariates on the other is worked out. To this end we employ generalized linear models (GLMs) for ordinal response variables and employ semiparametric estimation approaches and appropriate residual methods. It turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the data contain much more (timely and spatially) sequential structure than smooth space-time structure, (iii) a fine analysis of the individual sites in the area can be carried out with respect to predictive power of the covariates.

Studies on forest damage can generally not be carried out by common regression models, mainly for two reasons: Firstly, the response variable, damage state of trees, is usually observed in ordered categories. Secondly, responses are often correlated, either serially, as in a longitudinal study, or spatially, as in the application of this paper, where neighborhood interactions exist between damage states of spruces determined from aerial pictures. Thus so-called marginal regression models for ordinal responses, taking into account dependence among observations, are appropriate for correct inference. To this end we extend the binary models of Liang and Zeger (1986) and develop an ordinal GEE1 model, based on parametrizing association by global cross-ratios. The methods are applied to data from a survey conducted in Southern Germany. Due to the survey design, responses must be assumed to be spatially correlated. The results show that the proposed ordinal marginal regression models provide appropriate tools for analyzing the influence of covariates, that characterize the stand, on the damage state of spruce.