Log in

Relevant bibliographies by topics / Logit / Journal articles

To see the other types of publications on this topic, follow the link: Logit.

Journal articles on the topic 'Logit'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Logit.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Li, Mengyang, Fengguang Su, Ou Wu, and Ji Zhang. "Logit Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1359–66. http://dx.doi.org/10.1609/aaai.v36i2.20024.

Full text

Abstract:

Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) feature perturbation can improve the robustness or even generalization capability of learned models. However, limited studies have explicitly explored for the perturbation of logit vectors. This work discusses several existing methods related to logit perturbation. Based on a unified viewpoint between positive/negative data augmentation and loss variations incurred by logit perturbation, a new method is proposed to explicitly learn to perturb logits. A comparative analysis is conducted for the perturbations used in our and existing methods. Extensive experiments on benchmark image classification data sets and their long-tail versions indicated the competitive performance of our learning method. In addition, existing methods can be further improved by utilizing our method.

APA, Harvard, Vancouver, ISO, and other styles

2

Bushway, Shawn D. "Is There Any Logic to Using Logit." Criminology & Public Policy 12, no. 3 (August 2013): 563–67. http://dx.doi.org/10.1111/1745-9133.12059.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (July 2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.

Full text

Abstract:

For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.

APA, Harvard, Vancouver, ISO, and other styles

4

Baser, O., and S. Long. "PMC11 WHICH ONE IS LOGICAL? LOGIT OR RARE EVENT LOGIT (RE-LOGIT)." Value in Health 8, no. 3 (May 2005): 378. http://dx.doi.org/10.1016/s1098-3015(10)63015-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ferraioli, Diodato. "Logit dynamics." ACM SIGecom Exchanges 12, no. 1 (June 2013): 34–37. http://dx.doi.org/10.1145/2509013.2509018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Washington, Simon, Peter Congdon, Matthew G. Karlaftis, and Fred L. Mannering. "Bayesian Multinomial Logit." Transportation Research Record: Journal of the Transportation Research Board 2136, no. 1 (January 2009): 28–36. http://dx.doi.org/10.3141/2136-04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Gatignon, Hubert, and David J. Reibstein. "Pooling Logit Models." Journal of Marketing Research 23, no. 3 (August 1986): 281. http://dx.doi.org/10.2307/3151486.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Uberti, Luca J. "Interpreting logit models." Stata Journal: Promoting communications on statistics and Stata 22, no. 1 (March 2022): 60–76. http://dx.doi.org/10.1177/1536867x221083855.

Full text

Abstract:

The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios. I also show that interaction terms are typically easier to interpret in practice than implied by the recent literature on this topic.

APA, Harvard, Vancouver, ISO, and other styles

9

Gatignon, Hubert, and David J. Reibstein. "Pooling Logit Models." Journal of Marketing Research 23, no. 3 (August 1986): 281–85. http://dx.doi.org/10.1177/002224378602300308.

Full text

Abstract:

The typical testing for equality of parameters across several response functions cannot be performed when the dependent variable is a probability. The authors investigate the pooling issues of the response function when the model is specified as a transformational logit. Various estimation methods are compared and an iterative generalized least squares procedure is proposed for testing the poolability of transformational logit response functions.

APA, Harvard, Vancouver, ISO, and other styles

10

COSTAIN, JAMES, and ANTON NAKOV. "Logit Price Dynamics." Journal of Money, Credit and Banking 51, no. 1 (October 7, 2018): 43–78. http://dx.doi.org/10.1111/jmcb.12559.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Kim, Dae-hak, and Hyeong-Chul Jeong. "Bootstrapping Logit Model." Communications for Statistical Applications and Methods 9, no. 1 (April 1, 2002): 281–89. http://dx.doi.org/10.5351/ckss.2002.9.1.281.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Gerken, J. "Generalized logit model." Transportation Research Part B: Methodological 25, no. 2-3 (April 1991): 75–88. http://dx.doi.org/10.1016/0191-2615(91)90015-b.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Gilbert, François, Patrice Marcotte, and Gilles Savard. "Logit network pricing." Computers & Operations Research 41 (January 2014): 291–98. http://dx.doi.org/10.1016/j.cor.2013.05.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Walker, Joan. "Mixed Logit (or Logit Kernel) Model: Dispelling Misconceptions of Identification." Transportation Research Record: Journal of the Transportation Research Board 1805, no. 1 (January 2002): 86–98. http://dx.doi.org/10.3141/1805-11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

NAGAKURA, DAISUKE, and MASAHITO KOBAYASHI. "TESTING THE SEQUENTIAL LOGIT MODEL AGAINST THE NESTED LOGIT MODEL." Japanese Economic Review 60, no. 3 (September 2009): 345–61. http://dx.doi.org/10.1111/j.1468-5876.2008.00458.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Chintagunta, Pradeep K. "Heterogeneous Logit Model Implications for Brand Positioning." Journal of Marketing Research 31, no. 2 (May 1994): 304–11. http://dx.doi.org/10.1177/002224379403100212.

Full text

Abstract:

The author discusses the implications of a heterogeneous logit model for brand positioning. The methodology presented is a restricted version of a mixture-of-logits model and obtains brand positions on a product-market map and the distribution of preferences across households while accounting for the effects of marketing variables on household brand choice behavior. The restriction involves imposing a factor structure on the covariance matrix of the distribution of intrinsic brand preferences. An empirical application of the methodology is presented using A.C. Nielsen household-level scanner panel data on the purchases of liquid laundry detergents. The results indicate that the proposed model provides a better fit to the data than the unrestricted mixture-of-logits model or the Choice Map methodology.

APA, Harvard, Vancouver, ISO, and other styles

17

Sa'diyah, Halimatus, and Riza Yuli Rusdiana. "MEMAHAMI PENGGUNAAN REGRESI PADA DATA RESPON MULTINOMIAL UNTUK PENELITIAN SOSIAL DAN KEPENDIDIKAN." FIBONACCI: Jurnal Pendidikan Matematika dan Matematika 7, no. 2 (January 9, 2022): 109. http://dx.doi.org/10.24853/fbc.7.2.109-126.

Full text

Abstract:

Model logit multinomial digunakan untuk memodelkan sifat hubungan antara peubah respon politomus dan peubah penjelas. Ada dua model logit multinomial untuk peubah respon politomus yang strukturnya tak berurut: model logit terampat dan model logit bersyarat. Kedua model mempunyai struktur serupa, , j = 1, …, k, tetapi berbeda dalam karakteristik peubah penjelasnya. Logit terampat menggunakan karakteristik dari individu (subyek) sebagai peubah penjelas, sedang logit bersyarat menggunakan karakteristik dari pilihan individu. Tulisan ini ingin menyajikan penggunaan keduanya dalam model regresi yang sering dibutuhkan dalam penelitian-penelitian sosial dan kependidikan. Ilustrasi melalui data hipotetik digunakan untuk memperjelas kebutuhan, kegunaan, metode analisis data sampai pada interpretasi hasil model regrgesi. Sajian komputasi yang ringkas dilakukan melalui dua software yang populer yaitu SAS untuk model regresi generalized-logit. Sedangkan untuk model regresi logit bersyarat dapat dilakukan dengan SAS dan SPSS. Baik SAS dan SPSS menyajikan hasil analsis regresi yang sama untuk model regresi logit bersyarat.

APA, Harvard, Vancouver, ISO, and other styles

18

Small, Kenneth A., and Cheng Hsiao. "Multinomial Logit Specification Tests." International Economic Review 26, no. 3 (October 1985): 619. http://dx.doi.org/10.2307/2526707.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Chintagunta, Pradeep K., and Alfred Demaris. "Logit Modeling: Practical Applications." Journal of Marketing Research 30, no. 3 (August 1993): 391. http://dx.doi.org/10.2307/3172890.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

ZHANG, JUNSEN, and SAUL D. HOFFMAN. "Discrete-Choice Logit Models." Sociological Methods & Research 22, no. 2 (November 1993): 193–213. http://dx.doi.org/10.1177/0049124193022002002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

de Grange, Louis, Felipe González, Ignacio Vargas, and Juan Carlos Muñoz. "A polarized logit model." Transportation Research Part A: Policy and Practice 53 (July 2013): 1–9. http://dx.doi.org/10.1016/j.tra.2013.06.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Shi, Haolun, and Guosheng Yin. "Boosting conditional logit model." Journal of Choice Modelling 26 (March 2018): 48–63. http://dx.doi.org/10.1016/j.jocm.2017.07.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Andersen, Steffen, Glenn W. Harrison, Arne Risa Hole, Morten Lau, and E. Elisabet Rutström. "Non-linear mixed logit." Theory and Decision 73, no. 1 (July 14, 2011): 77–96. http://dx.doi.org/10.1007/s11238-011-9277-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Lo, Andrew W. "Logit versus discriminant analysis." Journal of Econometrics 31, no. 2 (March 1986): 151–78. http://dx.doi.org/10.1016/0304-4076(86)90046-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

de Palma, André, and Karim Kilani. "Switching in the logit." Economics Letters 88, no. 2 (August 2005): 196–202. http://dx.doi.org/10.1016/j.econlet.2005.01.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Howel, Denise, and A. Demaris. "Logit Modelling: Practical Applications." Statistician 43, no. 1 (1994): 206. http://dx.doi.org/10.2307/2348949.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Alós-Ferrer, Carlos, and Nick Netzer. "The logit-response dynamics." Games and Economic Behavior 68, no. 2 (March 2010): 413–27. http://dx.doi.org/10.1016/j.geb.2009.08.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Daly, Andrew. "Estimating “tree” logit models." Transportation Research Part B: Methodological 21, no. 4 (August 1987): 251–67. http://dx.doi.org/10.1016/0191-2615(87)90026-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Gilbert, François, Patrice Marcotte, and Gilles Savard. "Mixed-logit network pricing." Computational Optimization and Applications 57, no. 1 (July 27, 2013): 105–27. http://dx.doi.org/10.1007/s10589-013-9585-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Nugraha, Jaka. "Studi Simulasi Model Nested Logit dan Paired Combinatorial Logit pada Respon Multinomial." Eksakta 13, no. 1-2 (February 3, 2016): 63–71. http://dx.doi.org/10.20885/eksakta.vol13.iss1-2.art8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Sarkar, S. K., Habshah Midi, and Sohel Rana. "Adequacy of Multinomial Logit Model with Nominal Responses over Binary Logit Model." Trends in Applied Sciences Research 6, no. 8 (August 1, 2011): 900–909. http://dx.doi.org/10.3923/tasr.2011.900.909.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Jian, Srinivas Peeta, Xiaozheng He, and Jinbao Zhao. "Combined multinomial logit modal split and paired combinatorial logit traffic assignment model." Transportmetrica A: Transport Science 14, no. 9 (February 2, 2018): 737–60. http://dx.doi.org/10.1080/23249935.2018.1431701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Ding, Hao, Ziwei Su, and Xiaoqian Liu. "A modified multinomial baseline logit model with logit functions having different covariates." Communications in Statistics - Simulation and Computation 49, no. 11 (January 21, 2019): 2861–75. http://dx.doi.org/10.1080/03610918.2018.1529238.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Yang, Heeyoon, Gahyung Kim, and Jee-Hyoung Lee. "Logit Averaging: Capturing Global Relation for Session-Based Recommendation." Applied Sciences 12, no. 9 (April 22, 2022): 4256. http://dx.doi.org/10.3390/app12094256.

Full text

Abstract:

Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments.

APA, Harvard, Vancouver, ISO, and other styles

35

Schmidheiny, Kurt, and Marius Brülhart. "On the equivalence of location choice models: Conditional logit, nested logit and Poisson." Journal of Urban Economics 69, no. 2 (March 2011): 214–22. http://dx.doi.org/10.1016/j.jue.2010.09.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Wei, Fulu, Zhenggan Cai, Zhenyu Wang, Yongqing Guo, Xin Li, and Xiaoyan Wu. "Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model." Applied Sciences 11, no. 17 (August 25, 2021): 7819. http://dx.doi.org/10.3390/app11177819.

Full text

Abstract:

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

APA, Harvard, Vancouver, ISO, and other styles

37

Jones, Stewart, and David A. Hensher. "Predicting Firm Financial Distress: A Mixed Logit Model." Accounting Review 79, no. 4 (October 1, 2004): 1011–38. http://dx.doi.org/10.2308/accr.2004.79.4.1011.

Full text

Abstract:

Over the past three decades the literature on financial distress prediction has largely been confined to simple multiple discriminant analysis, binary logistic or probit analysis, or rudimentary multinomial logit models (MNL). There has been a conspicuous absence of modeling innovation in this literature as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. In particular, there has been no recognition of major advances in discrete choice modeling over the last 15 years, which has increasingly relaxed behaviorally questionable assumptions associated with the independently and identically distributed errors (IID) condition and allowed for observed and unobserved heterogeneity. In contrast to standard logit, the mixed logit model fulfils this purpose and provides a superior framework for explanation and prediction. We explain the theoretical and econometric underpinnings of mixed logit and demonstrate its empirical usefulness in the context of a specific but topical area of accounting research: financial distress prediction. Comparisons of model-fits and out-of-sample forecasts indicate that mixed logit outperforms standard logit by significant margins. While mixed logit has valuable applications in financial distress research, its potential usefulness in other areas of accounting research should not be overlooked.

APA, Harvard, Vancouver, ISO, and other styles

38

Deauvieau, Jérôme. "Comparer les résultats d’un modèle logit dichotomique ou polytomique entre plusieurs groupes à partir des probabilités estimées." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 142, no. 1 (April 2019): 7–31. http://dx.doi.org/10.1177/0759106319834657.

Full text

Abstract:

Il est très fréquent en sociologie de chercher à comparer les effets d’une variable d’un même modèle logit réalisé sur plusieurs groupes. La manière la plus courante de réaliser cette opération a souvent consisté à comparer directement la valeur du coefficient logit de la variable d’intérêt. Or, cette pratique soulève de sérieuses objections méthodologiques dans le cas de la modélisation logit. Ce point a été mis en évidence dans la littérature sociologique par Allison à la fin des années 1990. De la fin des années 2000 jusqu’à aujourd’hui, un nombre conséquent de travaux ont été consacrés à ce sujet. Notre objectif ici est de traiter cette question dans le cas où la variable d’intérêt est catégorielle et dans un cadre général regroupant la modélisation logit dichotomique et polytomique. Les solutions discutées dans cet article consistent à passer par une traduction du coefficient logit sous forme de probabilités. Après avoir rappelé les données du problème et les différents registres de solutions proposées, nous étudions le comportement de deux méthodes de traduction d’un coefficient logit, l’écart expérimental et l’écart pur, du point de vue de la comparaison entre plusieurs groupes des résultats d’une modélisation logit.

APA, Harvard, Vancouver, ISO, and other styles

39

Opic, Sinisa. "Specifics of logit and probit regression in education sciences - why wouldn't we use it?" Cypriot Journal of Educational Sciences 15, no. 6 (December 31, 2020): 1557–68. http://dx.doi.org/10.18844/cjes.v15i6.5305.

Full text

Abstract:

Regression is one of the dominant analysis methods used in the social sciences and educational sciences. There are different regression methods based on the type of research that is being conducted. The probit and logit regression models are regression methods which are being used recently by most researchers. However, their interpretations are not straightfoward and most researchers end up misinterepreting the results from the probit and logit regression models. This research therefore aims to examine the differences between the probit and logit models, in comparison with other linear regression models. Using a comparative research design, this study utilises resources from previous researchers, hence, the study took a form of a literature review. The results of this study is essential to educational and social sciences researchers who make use of the probit, logit and other regression methods. The research also explains why logit and probit should be used in place of other regression models. Keywords: education sciences; Linear regression; Logit; Probit; Regression

APA, Harvard, Vancouver, ISO, and other styles

40

Liu, Bingquan, Yonghong Zhang, and Wei Du. "A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network." Mathematical Problems in Engineering 2020 (April 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/3702965.

Full text

Abstract:

This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network.

APA, Harvard, Vancouver, ISO, and other styles

41

Prashker, J. N., and S. Bekhor. "Investigation of Stochastic Network Loading Procedures." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 94–102. http://dx.doi.org/10.3141/1645-12.

Full text

Abstract:

The network loading process of stochastic traffic assignment is investigated. A central issue in the assignment problem is the behavioral assumption governing route choice, which concerns the definition of available routes and the choice model. These two problems are addressed and reviewed. Although the multinomial logit model can be implemented efficiently in stochastic network loading algorithms, the model suffers from theoretical drawbacks, some of them arising from the independence of irrelevant alternatives property. As a result, the stochastic loading on routes that share common links is overloaded at the overlapping parts of the routes. Other logit-family models recently have been proposed to overcome some of the theoretical problems while maintaining the convenient analytical structure. Three such models are investigated: the C-logit model, which was specifically defined for route choice; and two general discrete-choice models, the cross-nested logit model and the paired combinatorial logit model. The two latter models are adapted to route choice, and simple network examples are presented to illustrate the performance of the models with respect to the overlapping problem. The results indicate that all three models perform better than does the multinomial logit model. The cross-nested logit model has an advantage over the two other generalized models because it enables performing stochastic loading without route enumeration. The integration of this model with the stochastic equilibrium problem is discussed, and a specific algorithm using the cross-nest logit model is presented for the stochastic loading phase.

APA, Harvard, Vancouver, ISO, and other styles

42

Sándor, Zsolt. "Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums." Acta Universitatis Sapientiae, Economics and Business 1, no. 1 (July 1, 2013): 85–108. http://dx.doi.org/10.2478/auseb-2014-0006.

Full text

Abstract:

Abstract We study Monte Carlo simulation in some recent versions of random coefficient logit models that contain large sums of expressions involving multivariate integrals. Such large sums occur in the random coefficient logit with demographic characteristics, the random coefficient logit with limited consumer information and the design of choice experiments for the panel mixed logit. We show that certain quasi-Monte Carlo methods, that is, so-called (t, m, s)-nets, provide improved performance over pseudo-Monte Carlo methods in terms of bias, standard deviation and root mean squared error.

APA, Harvard, Vancouver, ISO, and other styles

43

Czine, Péter. "A diszkrét választási kísérlet elméleti áttekintése." International Journal of Engineering and Management Sciences 5, no. 1 (April 14, 2020): 62–73. http://dx.doi.org/10.21791/ijems.2020.1.6.

Full text

Abstract:

Jelen tanulmány egy preferenciaértékelési módszer, a diszkrét választási kísérlet bemutatását célozza. Az olvasó információt nyerhet a módszer hátterével, alkalmazási területeivel (külön hangsúlyt fektetve az egészség-gazdaságosság területére), folyamatával és modelljei közül hárommal (multinomiális logit, random paraméterű logit, nested logit) kapcsolatosan. A szakirodalmi elemzés eredményei alapján elmondható, hogy a módszer rendkívül ígéretes, viszont számos területen még fejlesztésre szorul.

APA, Harvard, Vancouver, ISO, and other styles

44

Kajita, Yoshitaka. "Proposals of AHP-Type of Disaggregate Logit Model and Improved AHP Model in Modal Choice." Applied Mechanics and Materials 409-410 (September 2013): 1166–72. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1166.

Full text

Abstract:

This paper aims at constructing an improved model of modal choice by the use of both of AHP model and disaggregate logit model. Firstly, using AHP structure model, the importance of various factors in modal choice is investigated, and relevant choosing consciousness is scored. Secondly, a disaggregate logit model for modal choice with AHP scores and physical factors is proposed. Thirdly, weights of items in AHP model for modal choice are reversibly estimated by the use of the proposed AHP-type of disaggregate logit model. Direct AHP model, AHP-type of disaggregate logit model and improved AHP model are also compared each other.

APA, Harvard, Vancouver, ISO, and other styles

45

Schaak, Henning, and Oliver Mußhoff. "Public Preferences for Pasture Landscapes and the Role of Scale Heterogeneity." German Journal of Agricultural Economics 70, no. 3 (September 1, 2021): 182–91. http://dx.doi.org/10.30430/70.2021.3.182-191.

Full text

Abstract:

The paper investigates the influence of different model specifications for interpreting the results of discrete choice experiments when investigating heterogeneous public landscape preferences. Comparing model specifications based on the Mixed Multinomial Logit and the Generalized Multinomial Logit Model reveals that the parameter estimates appear qualitatively comparable. Still, a more in-depth investigation of the conditional estimate distributions of the sample show that parameter interactions in the Generalized Multinomial Logit Model lead to different interpretations compared to the Mixed Multinomial Logit Model. This highlights the potential impact of common model specifications in the results in landscape preference studies.

APA, Harvard, Vancouver, ISO, and other styles

46

Jones, Bradford S., and Regina P. Branton. "Beyond Logit and Probit: Cox Duration Models of Single, Repeating, and Competing Events for State Policy Adoption." State Politics & Policy Quarterly 5, no. 4 (December 2005): 420–43. http://dx.doi.org/10.1177/153244000500500406.

Full text

Abstract:

Since 1990, the standard statistical approach for studying state policy adoption has been an event history analysis using binary link models, such as logit or probit. In this article, we evaluate this logit-probit approach and consider some alternative strategies for state policy adoption research. In particular, we discuss the Cox model, which avoids the need to parameterize the baseline hazard function and, therefore, is often preferable to the logit-probit approach. Furthermore, we demonstrate how the Cox model can be modified to deal effectively with repeatable and competing events, events that the logit-probit approach cannot be used to model.

APA, Harvard, Vancouver, ISO, and other styles

47

Andrews, Rick L., and Ajay K. Manrai. "Simulation Experiments in Choice Simplification: The Effects of Task and Context on Forecasting Performance." Journal of Marketing Research 35, no. 2 (May 1998): 198–209. http://dx.doi.org/10.1177/002224379803500206.

Full text

Abstract:

Experimental and protocol-based research has demonstrated convincingly that consumers frequently use simplification heuristics prior to making choices. Consequently, quantitative choice models incorporating simplification strategies recently have received much research attention. Given the additional computational cost and complexity involved in estimating these models, the authors investigate how robustly the standard logit model accommodates choice simplification processes. The results show that when consumers screen brands using an elimination-by-aspects strategy, logit forecasts choices reasonably well, but the parameter estimates are severely biased. However, when consumers screen through brands previously purchased, augmented by brands promoted, logit both forecasts poorly and produces biased parameter estimates, even when dedicated screening variables are included in the logit utility function. The predictive validity of logit improves somewhat when the universal set size is small and when purchase event feedback is less important.

APA, Harvard, Vancouver, ISO, and other styles

48

Vlaovic-Begovic, Sanja. "Logit models for predicting bankruptcy." Skola biznisa, no. 2 (2017): 137–49. http://dx.doi.org/10.5937/skolbiz2-16523.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Fudenberg, Drew, and Tomasz Strzalecki. "Dynamic Logit With Choice Aversion." Econometrica 83, no. 2 (2015): 651–91. http://dx.doi.org/10.3982/ecta11846.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Bonett, Douglas G. "The negative multinomial logit model." Communications in Statistics - Theory and Methods 14, no. 7 (January 1985): 1713–17. http://dx.doi.org/10.1080/03610928508829007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!