scholarly journals Comparation of logistic regression methods and discrete choice model in the selection of habitats

2010 ◽  
Vol 67 (3) ◽  
pp. 327-333
Author(s):  
Sandra Vergara Cardozo ◽  
Bryan Frederick John Manly ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Logistic Regression ◽  
Discrete Choice ◽  
Resource Selection ◽  
Choice Model ◽  
The United States ◽  
Discrete Choice Models ◽  
Selection Function ◽  
Data Set ◽  
North West ◽  
Selection Of

Based on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States.

Modeling Store Choices with Cross-Sectional and Pooled Cross-Sectional Data: A Comparison

1995 ◽  
Vol 27 (8) ◽  
pp. 1303-1315 ◽  
Author(s):  
J-C Thill
Keyword(s):  
Market Share ◽  
Discrete Choice ◽  
Marginal Utility ◽  
Choice Model ◽  
Practical Implication ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Destination Choice ◽  
Cross Sectional ◽  
Data Set

Contrary to many other types of spatial decisions, shopping destination choice behavior is highly repetitive. For the practitioner looking for good predictors of store patronage, for reliable marginal utility estimates and reliable market share predictions, a central concern is with the type of data best suited to the research question, given the existing logistic and financial constraints. Different approaches can be recognized in the literature in which conventional discrete choice models are applied to shopping destination choice problems. In this paper, two of the most common practices are assessed and compared. First, the choice model is estimated with all choices of a relevant destination observed during a certain period of time (pooled cross-sectional data). The alternative approach consists in an estimation with the choice of the destination where the majority of purchases takes place (cross-sectional data). In the particular data set employed here, no evidence is found to support the idea that a multinomial logit model estimated with cross-sectional data does not perform as well as a model estimated with pooled cross-sectional data. Both models are found to be similar in their ability to identity the main predictors of store choice. Models developed on either data sets have marginal utility estimates that exhibit no statistically significant differences. Finally, market share predictions derived from both models are not statistically different. It appears, therefore, that there is no need to collect repeated patronage data over an extended period of time. The practitioner who wishes to use a conventional discrete choice model may avoid spending much time and money by gathering limited data on regular patronage patterns. In addition to this practical implication, the conclusions suggest that regular shopping destinations are chosen in accordance with the same behavioral motives as ancillary destinations are.

Effects of Less-Equilibrated Data on Travel Choice Model Estimation

2003 ◽  
Vol 1831 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Jun-Seok Oh ◽  
Cristián E. Cortés ◽  
Will Recker
Keyword(s):  
Monte Carlo Simulation ◽  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Adjustment Process ◽  
Model Estimation ◽  
Real World Data ◽  
Simulation Experiments ◽  
Data Set ◽  
Travel Choice

Most discrete choice models assume steady state conditions and a fully equilibrated system when estimating unknown coefficients from real-world data. However, the estimated model can be biased when the data set used for the model estimation was drawn from non- or less-equilibrated traveler behavior. The resulting biased model could lead to a misunderstanding of the system. Such effects on discrete choice model estimation were examined by performing Monte Carlo simulation experiments. A day-to-day dynamic evolutionary framework was used to observe changes in traveler’s choice and to compare the estimated results during the adjustment process with the true behavior parameters.

scholarly journals Altitude and risk of sudden unexpected infant death in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard Johnston ◽  
Xiaohan Yan ◽  
Tatiana M. Anderson ◽  
Edwin A. Mitchell
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Infant Death ◽  
The United States ◽  
Sudden Infant ◽  
Data Set ◽  
Live Births ◽  
Sudden Unexpected Infant Death ◽  
Increased Risk ◽  
Unexpected Infant Death

AbstractThe effect of altitude on the risk of sudden infant death syndrome (SIDS) has been reported previously, but with conflicting findings. We aimed to examine whether the risk of sudden unexpected infant death (SUID) varies with altitude in the United States. Data from the Centers for Disease Control and Prevention (CDC)’s Cohort Linked Birth/Infant Death Data Set for births between 2005 and 2010 were examined. County of birth was used to estimate altitude. Logistic regression and Generalized Additive Model (GAM) were used, adjusting for year, mother’s race, Hispanic origin, marital status, age, education and smoking, father’s age and race, number of prenatal visits, plurality, live birth order, and infant’s sex, birthweight and gestation. There were 25,305,778 live births over the 6-year study period. The total number of deaths from SUID in this period were 23,673 (rate = 0.94/1000 live births). In the logistic regression model there was a small, but statistically significant, increased risk of SUID associated with birth at > 8000 feet compared with < 6000 feet (aOR = 1.93; 95% CI 1.00–3.71). The GAM showed a similar increased risk over 8000 feet, but this was not statistically significant. Only 9245 (0.037%) of mothers gave birth at > 8000 feet during the study period and 10 deaths (0.042%) were attributed to SUID. The number of SUID deaths at this altitude in the United States is very small (10 deaths in 6 years).

The Interplay Between Online Reviews and Physician Demand: An Empirical Investigation

2021 ◽  
Author(s):  
Yuqian Xu ◽  
Mor Armony ◽  
Anindya Ghose
Keyword(s):  
Service Quality ◽  
Operations Management ◽  
Choice Model ◽  
Healthcare Delivery ◽  
Online Reviews ◽  
The United States ◽  
Patient Choice ◽  
Data Set ◽  
Textual Information ◽  
Time Service

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

scholarly journals Factors That Influence the Selection of the Solo Soemarmo Airport Train Mode

2018 ◽  
Vol 2 (2) ◽  
pp. 127-132
Author(s):  
Sapto Priyanto
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Sampling Techniques ◽  
Master Plan ◽  
Railway Network ◽  
Logical Regression ◽  
The Republic ◽  
The Government ◽  
Willingness To Use ◽  
Selection Of

The Government through the National Railway Master Plan has launched the development of the Airport Railway Network and Services to facilitate passenger mobility, one of which is the construction of the Adi Soemarmo Airport train. In April 2017 a groundbreaking project for the development of the Adi Soemarmo Airport, Boyolali District by the President of the Republic of Indonesia was scheduled to be operational in 2019. This study uses a discrete choice model to express the opportunities of each passenger to use the airport train. The research instruments were prepared using predictor variables developed from service dimensions according to Gaspers. The sample used was 200 respondents with random sampling techniques. The data collected is processed using a binary logical regression model because the response variable is in the form of a dichotomy. The results showed the accuracy of the train schedule and affordability of train fares affect the willingness to use airport trains.

scholarly journals A DISCRETE CHOICE MODEL FOR FIRM LOCATION DECISION

2016 ◽  
Vol 14 (4) ◽  
Author(s):  
Noordini Che Man ◽  
Harry Timmerman
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Central Business District ◽  
Statistical Technique ◽  
Discrete Choice Models ◽  
Location Decisions ◽  
Firm Location ◽  
Suitability Analysis ◽  
Choice Data ◽  
Business District

Where to locate? It is one of the most important question in locating a business in a city. In the city center, business or firms are functioning as a dominant attractor of employment and also employment locations which linked the land use and transportation system. The objective of this paper is to describe the location model of firms in Kuala Lumpur area. Two important determinants of location choice model in this study are the accessibility measures and the suitability analysis indicators. The model focuses on the statistical technique for analyzing discrete choice data by using econometric and Geographic Information System software. The findings in this paper show that agriculture, mining, electricity, gas and water, transport and finance firms' type are mostly located outside of Kuala Lumpur's Central Business District area. Meanwhile, manufacturing, construction and wholesale firms' type are located in the Central Business District area. The result of this study will highlight the use of discrete choice models in the analysis of firm location decisions which will be a foundation to facilitate town planners and decision makers to understand the firm location decisions in their region.

scholarly journals Measuring hospital spatial wingspan by using a discrete choice model with utility-threshold

2018 ◽  
Author(s):  
Saley Issa ◽  
Ribatet Mathieu ◽  
Molinari Nicolas
Keyword(s):  
Monte Carlo ◽  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Mcmc Methods ◽  
Hospital Competition ◽  
Policy Makers ◽  
New Approach ◽  
Asthma Patients ◽  
The One

AbstractPolicy makers increasingly rely on hospital competition to incentivize patients to choose high-value care. Travel distance is one of the most important drivers of patients’ decision. The paper presents a method to numerically measure, for a given hospital, the distance beyond which no patient is expected to choose the hospital for treatment by using a new approach in discrete choice models. To illustrate, we compared 3 hospitals attractiveness related to this distance for asthma patients admissions in 2009 in Hérault (France), showing, as expected, CHU Montpellier is the one with the most important spatial wingspan. For estimation, Monte Carlo Markov Chain (MCMC) methods are used.

Pricing Competition Under Specific Discrete Choice Models

2020 ◽  
Vol 37 (02) ◽  
pp. 2050008
Author(s):  
Farhad Etebari
Keyword(s):  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Public Information ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Discrete Choice Models ◽  
Nested Logit ◽  
Nested Logit Model ◽  
Substitution Pattern

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.

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