Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation

2003 ◽  
Vol 30 (2) ◽  
pp. 191-204 ◽  
Author(s):  
Man-Suk Oh ◽  
Jung Whan Choi ◽  
Dai-Gyoung Kim
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Market Segmentation ◽  
Latent Class ◽  
Logit Models ◽  
Latent Class Logit ◽  
Parameter Constraints

scholarly journals Farmer preferences for adopting precision farming technologies: a case study from Italy

2020 ◽  
Author(s):  
J Blasch ◽  
B van der Kroon ◽  
P van Beukering ◽  
R Munster ◽  
S Fabiani ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Choice Experiment ◽  
Latent Class ◽  
Precision Farming ◽  
Logit Models ◽  
Willingness To Adopt ◽  
Latent Class Logit ◽  
Fertiliser Use

Abstract Precision farming (PF) technologies can help to mitigate the environmental impact of agriculture by reducing fertiliser use and irrigation while saving cost for the farmer. However, these technologies are not widely adopted in Europe. We study farmers’ willingness to adopt PF technologies based on a choice experiment. Among other determinants, we explore the role of social influence for the valuation of PF technology features. The data are analysed using mixed and latent class logit models. Our results show that knowledge of fellow farmers who adopted the technology positively influences the valuation of PF technology features, stressing the importance of networks.

scholarly journals Using Market Segmentation Analysis to Identify Consumer Preferences for Fresh Peach Attributes

HortScience ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1664-1668 ◽  
Author(s):  
Ruchen Zhou ◽  
Chengyan Yue ◽  
Shuoli Zhao ◽  
R. Karina Gallardo ◽  
Vicki McCracken ◽  
...  
Keyword(s):  
Market Segmentation ◽  
Choice Experiment ◽  
Online Survey ◽  
Latent Class ◽  
Consumer Preferences ◽  
The United States ◽  
Peach Fruit ◽  
Segmentation Analysis ◽  
Heterogeneous Preference ◽  
Latent Class Logit

Consumer preferences for attributes of fresh peach fruit in the United States are largely unknown on a national basis. We used a choice experiment to explore market segmentation based on consumer heterogeneous preference for fruit attributes including external color, blemish, firmness, sweetness, flavor, and price. We collected the data using an online survey with 800 U.S. consumers. Using a latent class logit model, we identified three segments of consumers differing by different sets of preferred quality attributes: experience attribute-oriented consumers, who valued fruit quality (48.8% of the sample); search attribute-oriented consumers, who valued fruit appearance (33.7% of the sample); and balanced consumers, who considered search attributes and experience attributes but who valued each in a balanced way (17.5% of the sample). Each group demonstrated differentiated demographics and purchasing habits. The results have important marketing implications for peach breeders and suppliers.

scholarly journals Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for Rt Estimation

2021 ◽  
Author(s):  
Xian Yang ◽  
Shuo Wang ◽  
Yuting Xing ◽  
Ling Li ◽  
Richard Yi Da Xu ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Reproduction Number ◽  
State Of The Art ◽  
Transmission Dynamics ◽  
Selection Mechanism ◽  
Problem Of Time ◽  
Joint Inference ◽  
Abrupt Changes ◽  
Bayesian Smoothing

Abstract In epidemiological modelling, the instantaneous reproduction number, Rt, is important to understand the transmission dynamics of infectious diseases. Current Rt estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of Rt. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for Rt estimation, resulting in the state-of-the-art ‘DARt’ system for Rt estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.

scholarly journals Multilevel latent class modeling to segment the microfinance market

2019 ◽  
Vol 37 (5) ◽  
pp. 1103-1118
Author(s):  
Lucas Lopes Ferreira Souza ◽  
Francesca Bassi ◽  
Ana Augusta Ferreira de Freitas
Keyword(s):  
Market Segmentation ◽  
Financial Services ◽  
Latent Class ◽  
Latent Class Models ◽  
Microfinance Institutions ◽  
Content Type ◽  
Business Results ◽  
Latent Class Modeling ◽  
Class Models ◽  
Do So

Purpose Microfinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existence of diverse types of microfinance organizations and client profiles. Microfinance institutions must primarily pay more attention to the client, and to the mechanism through which financial services are delivered. The purpose of this paper is to identify the profiles of microfinance customers and the features of their operations. Design/methodology/approach In this paper, multilevel latent class models were estimated to reveal clusters of operations and classes of clients. Findings The results show that there are six clusters of operations and four classes of clients in the market, each with distinct profiles and needs. Different strategies are recommended for each cluster and class. Originality/value Numerous studies have focused on the importance of getting to know the clients of microfinance programs, but none as yet have used market segmentation as a way to do so. The goal is to generate better strategies to help clients improve their business results. Applying market segmentation to the microfinance market may point to different products for different groups of clients, taking the real needs of each of them into account.

scholarly journals Bayesian Inference and Testing Any Hypothesis You Can Specify

2018 ◽  
Vol 1 (2) ◽  
pp. 281-295 ◽  
Author(s):  
Alexander Etz ◽  
Julia M. Haaf ◽  
Jeffrey N. Rouder ◽  
Joachim Vandekerckhove
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Hypothesis Testing ◽  
Likelihood Ratio ◽  
Special Form ◽  
Null Hypothesis ◽  
Bayes Factor ◽  
Alternative Hypotheses ◽  
Competing Models

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model-selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that the researcher specify exactly what is meant by a “null” hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, but in all cases the inferential procedure is based on the same underlying method of likelihood comparison. An associated app can be found at https://osf.io/mvp53/ . This article is the work of the authors and is reformatted from the original, which was published under a CC-By Attribution 4.0 International license and is available at https://psyarxiv.com/wmf3r/ .

scholarly journals Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

2020 ◽  
Vol 128 (5) ◽  
pp. 054105 ◽  
Author(s):  
Rama K. Vasudevan ◽  
Kyle P. Kelley ◽  
Eugene Eliseev ◽  
Stephen Jesse ◽  
Hiroshi Funakubo ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Dynamic Model ◽  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Probe Microscopy ◽  
Optimal Dynamic

scholarly journals Critical Perspective of Animal Production Specialists on Cell-Based Meat in Brazil: From Bottleneck to Best Scenarios

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1678
Author(s):  
Marina S. Heidemann ◽  
Cesar A. Taconeli ◽  
Germano G. Reis ◽  
Giuliana Parisi ◽  
Carla F. M. Molento
Keyword(s):  
Online Survey ◽  
Latent Class ◽  
Animal Production ◽  
Consumer Perception ◽  
Meat Production ◽  
Logit Models ◽  
Negative Consequences ◽  
Farm Animal Welfare ◽  
Critical Perspective ◽  
Novel Technology

Recently, many studies regarding consumer perception of cell-based meat have been published. However, the opinion of the professionals involved in animal production also seems relevant. In particular, veterinarians and animal scientists may be important players in the new cell-based meat production, acting as proponents or barriers to this major improvement for farm animal welfare. Therefore, our aim is to analyse the knowledge and perspective of Brazilian veterinarians and animal scientists regarding cell-based meat. Veterinarians (76.8%; 209/272) and animal scientists (23.2%; 63/272) responded to an online survey. Logistic regression, latent class and logit models were used to evaluate objective answers, and the Discourse of the Collective Subject method was used to interpret open-ended answers. Specialists who were women (62.5%; 170/272), veterinarians (76.8%; 209/272), vegetarians (7.0%; 19/272) and vegans (1.1%; 3/272) were more supportive of cell-based meat. Lack of knowledge and the connection with artificiality, the most frequent spontaneous word associated with cell-based meat by all respondents, were the main negative points highlighted. Thus, it seems fundamental to offer higher education to veterinarians and animal scientists regarding cell-based meat, since engaging them with this novel technology may mitigate both the resistance and its negative consequences for the professionals, society, the animals involved and the environment.

scholarly journals Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Juan Li ◽  
Boyu Jiang ◽  
Chunjiao Dong ◽  
Jue Wang ◽  
Xuan Zhang
Keyword(s):  
High Risk ◽  
Reliability Index ◽  
Latent Class ◽  
Signalized Intersections ◽  
Low Risk ◽  
New Classification ◽  
Intersection Safety ◽  
Dangerous Driving ◽  
Latent Class Logit ◽  
Risk Categories

Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.

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