Log in

Relevant bibliographies by topics / Estimation and inference

Academic literature on the topic 'Estimation and inference'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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

Select a source type:

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Estimation and inference.'

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.

Journal articles on the topic "Estimation and inference"

1

Tahk, Alexander. "Nonparametric Ideal-Point Estimation and Inference." Political Analysis 26, no. 2 (March 8, 2018): 131–46. http://dx.doi.org/10.1017/pan.2017.38.

Full text

Abstract:

Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

APA, Harvard, Vancouver, ISO, and other styles

2

Xiang, Ning, and Christopher Landschoot. "Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics." Entropy 21, no. 6 (June 10, 2019): 579. http://dx.doi.org/10.3390/e21060579.

Full text

Abstract:

This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower level of inference, Bayesian parameter estimation. This work formulates signal models using spherical harmonic beamforming that encodes the prior information on the sensor arrays in the form of analytical models with an unknown number of sound sources, and their locations. Available information on differences between the model and the sound signals as well as prior information on directions of arrivals are incorporated based on the principle of the maximum entropy. Two and three simultaneous sound sources have been experimentally tested without prior information on the number of sources. Bayesian inference provides unambiguous estimation on correct numbers of sources followed by the DoA estimations for each individual sound sources. This paper presents the Bayesian formulation, and analysis results to demonstrate the potential usefulness of the model-based Bayesian inference for complex acoustic environments with potentially multiple simultaneous sources.

APA, Harvard, Vancouver, ISO, and other styles

3

Frazier, David, and Eric Renault. "Indirect Inference: Which Moments to Match?" Econometrics 7, no. 1 (March 19, 2019): 14. http://dx.doi.org/10.3390/econometrics7010014.

Full text

Abstract:

The standard approach to indirect inference estimation considers that the auxiliary parameters, which carry the identifying information about the structural parameters of interest, are obtained from some recently identified vector of estimating equations. In contrast to this standard interpretation, we demonstrate that the case of overidentified auxiliary parameters is both possible, and, indeed, more commonly encountered than one may initially realize. We then revisit the “moment matching” and “parameter matching” versions of indirect inference in this context and devise efficient estimation strategies in this more general framework. Perhaps surprisingly, we demonstrate that if one were to consider the naive choice of an efficient Generalized Method of Moments (GMM)-based estimator for the auxiliary parameters, the resulting indirect inference estimators would be inefficient. In this general context, we demonstrate that efficient indirect inference estimation actually requires a two-step estimation procedure, whereby the goal of the first step is to obtain an efficient version of the auxiliary model. These two-step estimators are presented both within the context of moment matching and parameter matching.

APA, Harvard, Vancouver, ISO, and other styles

4

Lu, Jiannan. "Sharpening randomization-based causal inference for 22 factorial designs with binary outcomes." Statistical Methods in Medical Research 28, no. 4 (December 5, 2017): 1064–78. http://dx.doi.org/10.1177/0962280217745720.

Full text

Abstract:

In medical research, a scenario often entertained is randomized controlled 22 factorial design with a binary outcome. By utilizing the concept of potential outcomes, Dasgupta et al. proposed a randomization-based causal inference framework, allowing flexible and simultaneous estimations and inferences of the factorial effects. However, a fundamental challenge that Dasgupta et al.’s proposed methodology faces is that the sampling variance of the randomization-based factorial effect estimator is unidentifiable, rendering the corresponding classic “Neymanian” variance estimator suffering from over-estimation. To address this issue, for randomized controlled 22 factorial designs with binary outcomes, we derive the sharp lower bound of the sampling variance of the factorial effect estimator, which leads to a new variance estimator that sharpens the finite-population Neymanian causal inference. We demonstrate the advantages of the new variance estimator through a series of simulation studies, and apply our newly proposed methodology to two real-life datasets from randomized clinical trials, where we gain new insights.

APA, Harvard, Vancouver, ISO, and other styles

5

Chen, Liqiong, Antonio F. Galvao, and Suyong Song. "Quantile Regression with Generated Regressors." Econometrics 9, no. 2 (April 12, 2021): 16. http://dx.doi.org/10.3390/econometrics9020016.

Full text

Abstract:

This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.

APA, Harvard, Vancouver, ISO, and other styles

6

Abdul Jalil, Nur Raihan, Nur Anisah Mohamed, and Rossita Mohamad Yunus. "Estimation in regret-regression using quadratic inference functions with ridge estimator." PLOS ONE 17, no. 7 (July 21, 2022): e0271542. http://dx.doi.org/10.1371/journal.pone.0271542.

Full text

Abstract:

In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model’s performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.

APA, Harvard, Vancouver, ISO, and other styles

7

Hahn, Jinyong, Zhipeng Liao, and Geert Ridder. "NONPARAMETRIC TWO-STEP SIEVE M ESTIMATION AND INFERENCE." Econometric Theory 34, no. 6 (April 25, 2018): 1281–324. http://dx.doi.org/10.1017/s0266466618000014.

Full text

Abstract:

This article studies two-step sieve M estimation of general semi/nonparametric models, where the second step involves sieve estimation of unknown functions that may use the nonparametric estimates from the first step as inputs, and the parameters of interest are functionals of unknown functions estimated in both steps. We establish the asymptotic normality of the plug-in two-step sieve M estimate of a functional that could be root-n estimable. The asymptotic variance may not have a closed form expression, but can be approximated by a sieve variance that characterizes the effect of the first-step estimation on the second-step estimates. We provide a simple consistent estimate of the sieve variance, thereby facilitating Wald type inferences based on the Gaussian approximation. The finite sample performance of the two-step estimator and the proposed inference procedure are investigated in a simulation study.

APA, Harvard, Vancouver, ISO, and other styles

8

Gao, Jing, Kehan Bai, and Wenhao Gui. "Statistical Inference for the Inverted Scale Family under General Progressive Type-II Censoring." Symmetry 12, no. 5 (May 5, 2020): 731. http://dx.doi.org/10.3390/sym12050731.

Full text

Abstract:

Two estimation problems are studied based on the general progressively censored samples, and the distributions from the inverted scale family (ISF) are considered as prospective life distributions. One is the exact interval estimation for the unknown parameter θ , which is achieved by constructing the pivotal quantity. Through Monte Carlo simulations, the average 90 % and 95 % confidence intervals are obtained, and the validity of the above interval estimation is illustrated with a numerical example. The other is the estimation of R = P ( Y < X ) in the case of ISF. The maximum likelihood estimator (MLE) as well as approximate maximum likelihood estimator (AMLE) is obtained, together with the corresponding R-symmetric asymptotic confidence intervals. With Bootstrap methods, we also propose two R-asymmetric confidence intervals, which have a good performance for small samples. Furthermore, assuming the scale parameters follow independent gamma priors, the Bayesian estimator as well as the HPD credible interval of R is thus acquired. Finally, we make an evaluation on the effectiveness of the proposed estimations through Monte Carlo simulations and provide an illustrative example of two real datasets.

APA, Harvard, Vancouver, ISO, and other styles

9

Zyphur, Michael J., and Frederick L. Oswald. "Bayesian Estimation and Inference." Journal of Management 41, no. 2 (August 11, 2013): 390–420. http://dx.doi.org/10.1177/0149206313501200.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Castro-Martín, Luis, María del Mar Rueda, and Ramón Ferri-García. "Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment." Mathematics 8, no. 11 (November 23, 2020): 2096. http://dx.doi.org/10.3390/math8112096.

Full text

Abstract:

This study introduces a general framework on inference for a general parameter using nonprobability survey data when a probability sample with auxiliary variables, common to both samples, is available. The proposed framework covers parameters from inequality measures and distribution function estimates but the scope of the paper is broader. We develop a rigorous framework for general parameter estimation by solving survey weighted estimating equations which involve propensity score estimation for units in the non-probability sample. This development includes the expression of the variance estimator, as well as some alternatives which are discussed under the proposed framework. We carried a simulation study using data from a real-world survey, on which the application of the estimation methods showed the effectiveness of the proposed design-based inference on several general parameters.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Estimation and inference"

1

Cho, Young Su. "Empirical [gamma]-divergence : estimation and inference /." Bonn, 2005. http://www.gbv.de/dms/zbw/493498524.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Taylor, Luke. "Essays in nonparametric estimation and inference." Thesis, London School of Economics and Political Science (University of London), 2017. http://etheses.lse.ac.uk/3569/.

Full text

Abstract:

This thesis consists of three chapters which represent my journey as a researcher during this PhD. The uniting theme is nonparametric estimation and inference in the presence of data problems. The first chapter begins with nonparametric estimation in the presence of a censored dependent variable and endogenous regressors. For Chapters 2 and 3 my attention moves to problems of inference in the presence of mismeasured data. In Chapter 1 we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalise the identification argument put forward in Altonji, Ichimura and Otsu (2012), construct a nonparametric estimator, characterise its asymptotic property, and conduct a Monte Carlo investigation to study its small sample properties. We show that the estimator is consistent and asymptotically normally distributed. Chapter 2 considers specification testing for regression models with errors-in-variables. In contrast to the method proposed by Hall and Ma (2007), our test allows general nonlinear regression models. Since our test employs the smoothing approach, it complements the nonsmoothing one by Hall and Ma in terms of local power properties. We establish the asymptotic properties of our test statistic for the ordinary and supersmooth measurement error densities and develop a bootstrap method to approximate the critical value. We apply the test to the specification of Engel curves in the US. Finally, some simulation results endorse our theoretical findings: our test has advantages in detecting high frequency alternatives and dominates the existing tests under certain specifications. Chapter 3 develops a nonparametric significance test for regression models with measurement error in the regressors. To the best of our knowledge, this is the first test of its kind. We use a ‘semi-smoothing’ approach with nonparametric deconvolution estimators and show that our test is able to overcome the slow rates of convergence associated with such estimators. In particular, our test is able to detect local alternatives at the √n rate. We derive the asymptotic distribution under i.i.d. and weakly dependent data, and provide bootstrap procedures for both data types. We also highlight the finite sample performance of the test through a Monte Carlo study. Finally, we discuss two empirical applications. The first considers the effect of cognitive ability on a range of socio-economic variables. The second uses time series data - and a novel approach to estimate the measurement error without repeated measurements - to investigate whether future inflation expectations are able to stimulate current consumption.

APA, Harvard, Vancouver, ISO, and other styles

3

Amjad, Muhammad Jehangir. "Sequential data inference via matrix estimation : causal inference, cricket and retail." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120190.

Full text

Abstract:

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 185-193).
This thesis proposes a unified framework to capture the temporal and longitudinal variation across multiple instances of sequential data. Examples of such data include sales of a product over a period of time across several retail locations; trajectories of scores across cricket games; and annual tobacco consumption across the United States over a period of decades. A key component of our work is the latent variable model (LVM) which views the sequential data as a matrix where the rows correspond to multiple sequences while the columns represent the sequential aspect. The goal is to utilize information in the data within the sequence and across different sequences to address two inferential questions: (a) imputation or "filling missing values" and "de-noising" observed values, and (b) forecasting or predicting "future" values, for a given sequence of data. Using this framework, we build upon the recent developments in "matrix estimation" to address the inferential goals in three different applications. First, a robust variant of the popular "synthetic control" method used in observational studies to draw causal statistical inferences. Second, a score trajectory forecasting algorithm for the game of cricket using historical data. This leads to an unbiased target resetting algorithm for shortened cricket games which is an improvement upon the biased incumbent approach (Duckworth-Lewis-Stern). Third, an algorithm which leads to a consistent estimator for the time- and location-varying demand of products using censored observations in the context of retail. As a final contribution, the algorithms presented are implemented and packaged as a scalable open-source library for the imputation and forecasting of sequential data with applications beyond those presented in this work.
by Muhammad Jehangir Amjad.
Ph. D.

APA, Harvard, Vancouver, ISO, and other styles

4

Zhou, Min. "The estimation and inference of complex models." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/387.

Full text

Abstract:

In this thesis, we investigate the estimation problem and inference problem for the complex models. Two major categories of complex models are emphasized by us, one is generalized linear models, the other is time series models. For the generalized linear models, we consider one fundamental problem about sure screening for interaction terms in ultra-high dimensional feature space; for time series models, an important model assumption about Markov property is considered by us. The first part of this thesis illustrates the significant interaction pursuit problem for ultra-high dimensional models with two-way interaction effects. We propose a simple sure screening procedure (SSI) to detect significant interactions between the explanatory variables and the response variable in the high or ultra-high dimensional generalized linear regression models. Sure screening method is a simple, but powerful tool for the first step of feature selection or variable selection for ultra-high dimensional data. We investigate the sure screening properties of the proposal method from theoretical insight. Furthermore, we indicate that our proposed method can control the false discovery rate at a reasonable size, so the regularized variable selection methods can be easily applied to get more accurate feature selection in the following model selection procedures. Moreover, from the viewpoint of computational efficiency, we suggest a much more efficient algorithm-discretized SSI (DSSI) to realize our proposed sure screening method in practice. And we also investigate the properties of these two algorithms SSI and DSSI in simulation studies and apply them to some real data analyses for illustration. For the second part, our concern is the testing of the Markov property in time series processes. Markovian assumption plays an extremely important role in time series analysis and is also a fundamental assumption in economic and financial models. However, few existing research mainly focused on how to test the Markov properties for the time series processes. Therefore, for the Markovian assumption, we propose a new test procedure to check if the time series with beta-mixing possesses the Markov property. Our test is based on the Conditional Distance Covariance (CDCov). We investigate the theoretical properties of the proposed method. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained, and the power of the test procedure under local alternative hypothesizes have been studied. Simulation studies are conducted to demonstrate the finite sample performance of our test.

APA, Harvard, Vancouver, ISO, and other styles

5

Bispham, Francesco Devere. "Estimation and inference with nonstationary panel data." Thesis, University of Hull, 2005. http://hydra.hull.ac.uk/resources/hull:5635.

Full text

Abstract:

This PhD thesis applies the time-series concepts of unit-roots and cointegration to nonstationary panel data. The first three chapters set the scene for what follows and together are the first methodological core of the thesis, on nonstationary panel data estimation and testing. In chapter 1 we consider the established panel unit root tests of Levin, Lin and Chu (2002) and Im, Pesaran and Shin (2003) and also Pesaran (2005) for cross-sectional dependence, with a panel of 20 OECD inflation rates. In chapter 2 we consider the established panel cointegration tests of Kao (1999), Pedroni (1999) and Larsson, Lyhagen and Lothgren (200 1) with a panel of 25 OECD exchange rates to test for long run PPP, again including cross-sectional dependence. In chapter 3 a more original contribution is given. We conduct an extensive empirical study of the long run determinants of consumption expenditure for a panel of 20 OECD countries. A panel data cointegrating regression is estimated using the panel DOLS and FMOLS estimators of Kao and Chiang (2000) and Pedroni (2000,2001). Using Bai and Kao (2005) we again consider cross-sectional dependence. The second methodological core is the statistical inference of nonstationary panel data, in the last two chapters. In chapter 4 is another original contribution using the bootstrap with nonstationary panel data. New bootstrap algorithms are presented for the panel DOLS estimators mentioned above and also the group-mean estimator of Pesaran and Smith (1995). In our last original contribution, in chapter 5, we consider the asymptotic properties of nonstationary panel data estimators. The asymptotic normality and asymptotic consistency of our panel FMOLS, DOLS and OLS estimators are proved for the simple case of the panel cointegrating regression with a constant intercept and trend. The new sequential limit asymptotic theory of Phillips and Moon (1999) is highlighted.

APA, Harvard, Vancouver, ISO, and other styles

6

Hall, A. "Estimation and inference in simultaneous equation models." Thesis, University of Warwick, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356473.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Grant, Nicky Lee. "Estimation & inference under non-standard conditions." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708488.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Callahan, Margaret D. "Bayesian Parameter Estimation and Inference Across Scales." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459523006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Lyons, Simon. "Inference and parameter estimation for diffusion processes." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10518.

Full text

Abstract:

Diffusion processes provide a natural way of modelling a variety of physical and economic phenomena. It is often the case that one is unable to observe a diffusion process directly, and must instead rely on noisy observations that are discretely spaced in time. Given these discrete, noisy observations, one is faced with the task of inferring properties of the underlying diffusion process. For example, one might be interested in inferring the current state of the process given observations up to the present time (this is known as the filtering problem). Alternatively, one might wish to infer parameters governing the time evolution the diffusion process. In general, one cannot apply Bayes’ theorem directly, since the transition density of a general nonlinear diffusion is not computationally tractable. In this thesis, we investigate a novel method of simplifying the problem. The stochastic differential equation that describes the diffusion process is replaced with a simpler ordinary differential equation, which has a random driving noise that approximates Brownian motion. We show how one can exploit this approximation to improve on standard methods for inferring properties of nonlinear diffusion processes.

APA, Harvard, Vancouver, ISO, and other styles

10

Goldman, Nicholas. "Statistical estimation of evolutionary trees." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239234.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Estimation and inference"

1

G, MacKinnon James, ed. Estimation and inference in econometrics. New York: Oxford University Press, 1993.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

White, Halbert. Estimation, inference, and specification analysis. Cambridge: Cambridge University Press, 1994.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Millar, Russell B. Maximum Likelihood Estimation and Inference. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470094846.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Tan, W. Y., and N. Balakrishnan, eds. Robust inference. New York, USA: M. Dekker, 1986.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Bayesian inference: Parameter estimation and decisions. Berlin: Springer, 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Harney, Hanns L. Bayesian inference: Parameter estimation and decisions. Berlin: Springer, 2002.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Balakrishnan, N. Order statistics and inference: Estimation methods. Boston: Academic Press, 1991.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Harney, Hanns L. Bayesian Inference: Parameter Estimation and Decisions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Garthwaite, Paul H. Statistical inference. 2nd ed. Oxford: Oxford University Press, 2002.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

T, Jolliffe I., and Jones Byron 1951-, eds. Statistical inference. Oxford: Oxford University Press, 2002.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Estimation and inference"

1

Tattar, Prabhanjan Narayanachar, and H. J. Vaman. "Inference—Estimation." In Survival Analysis, 61–88. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306979-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Ross, Gavin J. S. "Inference and Stable Transformations." In Nonlinear Estimation, 44–72. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ross, Gavin J. S. "The Geometry of Nonlinear Inference." In Nonlinear Estimation, 73–107. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Flores, Carlos A., and Xuan Chen. "Estimation and Inference." In Average Treatment Effect Bounds with an Instrumental Variable: Theory and Practice, 75–84. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2017-0_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mills, Terence C. "Estimation and Inference." In Analysing Economic Data, 148–65. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137401908_11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Okello, Gabriel Otieno. "Statistical Inference: Estimation." In Simplified Business Statistics Using SPSS, 193–230. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003292654-11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Agresti, Alan, and Maria Kateri. "Statistical Inference: Estimation." In Foundations of Statistics for Data Scientists, 105–60. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003159834-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Kiefer, Jack Carl. "Point Estimation." In Introduction to Statistical Inference, 158–245. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4613-9578-2_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Koch, Karl-Rudolf. "Point estimation." In Bayesian Inference with Geodetic Applications, 33–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0048704.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Kiefer, Jack Carl. "Linear Unbiased Estimation." In Introduction to Statistical Inference, 81–136. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4613-9578-2_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Estimation and inference"

1

Ferrero, Alessandro, Simona Salicone, and Grazia Todeschini. "Accounting Measurement Uncertainty in Fuzzy Inference." In 2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2007. http://dx.doi.org/10.1109/amuem.2007.4362574.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Tsai, Meng Hsiu, Nicole Marie Ely, and Yingfeng Wang. "Uncertainty Estimation for Twitter Inference." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zanobini, A., L. Ciani, and G. Pellegrini. "Quantifying the Measurement Uncertainty Using Bayesian Inference." In 2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2007. http://dx.doi.org/10.1109/amuem.2007.4362560.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Sparano, Joa˜o V., Eduardo A. Tannuri, Alexandre N. Simos, and Vini´cius L. F. Matos. "On the Estimation of Directional Wave Spectrum Based on Stationary Vessels 1st Order Motions: A New Set of Experimental Results." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57431.

Full text

Abstract:

The practicability of estimating directional wave spectra based on a vessel 1st order response has been recently addressed by several researchers. The interest is justified since on-board estimations would only require only a simple set of accelerometers and rate-gyros connected to an ordinary PC. The on-board wave inference based on 1st order motions is therefore an uncomplicated and inexpensive choice for wave estimation if compared to wave buoys and radar systems. The latest works in the field indicate that it is indeed possible to obtain accurate estimations and a Bayesian inference model seems to be the preferable method adopted for performing this task. Nevertheless, most of the previous analysis has been based exclusively on numerical simulations. At Polytechnic School, an extensive research program supported by Petrobras has been conducted since 2000, aiming to evaluate the possibility of estimating wave spectrum on-board offshore systems, like FPSO platforms. In this context, a series of small-scale tests has been performed at the LabOceano wave basin, comprising long and short crested seas. A possible candidate for on-board wave estimation has been recently studied: a crane barge (BGL) used for launching ducts offshore Brazil. The 1:48 model has been subjected to bow and quartering seas with different wave heights and periods and also different levels of directional spreading. A Bayesian inference method was adopted for evaluating the wave spectra based on the time-series of motions and the results were directly compared to the wave spectra measured in the basin by means of an array of wave probes. Very good estimations of the statistical parameters (significant wave height, peak period and mean wave direction) were obtained and, in most cases, even the directional spreading could be properly predicted. Inversion of the mean direction (180° shift), mentioned by some authors as a possible drawback of the Bayesian inference method, was not observed in any case. Sensitivity analysis on errors in the input parameters, such as the vessel inertial characteristics, has also been performed and attested that the method is robust enough to cope well with practical uncertainties. Overall results once again indicate a good performance of the inference method, providing an important additional validation supported by a large set of model tests.

APA, Harvard, Vancouver, ISO, and other styles

5

Wada, Ryota, and Takuji Waseda. "Likelihood-Weighted Method of General Pareto Distribution for Extreme Wave Height Estimation." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10792.

Full text

Abstract:

In designing ocean structures, estimating the largest wave height it may encounter over its lifetime is a critical issue, but wave observation data is often sparse in space and time. Because of the limited data available, estimation errors are inevitably large. For an economical and robust structure design, the probability density function of the extreme wave height and its confidence interval must be theoretically quantified from limited information available. Extreme values estimations have been made by finding the best fitting distribution from limited observations, and extrapolating it for the desired long period. Estimations based on frequentist method lack of generality in confidence interval estimations, especially when the data size is small. Another technique recently developed is based on Bayesian Statistics, which provides the inference of uncertainty. Previous studies use informative and non-informative priors and Markov Chain Monte Carlo (MCMC) simulation for estimation. We have developed a “Likelihood-Weighted Method (LWM)” to objectively evaluate probability density function of the extreme value. The method is based on Extreme Theory and Bayesian Statistics. Our attempt is to use the ignorant prior to relate each parameter set’s likelihood to its probability. This method is pragmatic, because the numerical implementation does not require the use of MCMC. The theoretical background and practical advantages of LWM are described. Examples from randomly produced data show the performance of this method, and application to real wave data reveals the poor estimations of previous methods that do not use the Bayesian theorem. The quantification of probability for each extreme value distribution enables the probability-weighted evaluation for inference such as maximum wave height probability density function. The new inference derived from this method is useful to change structure design methodologies of ocean structures.

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Chenzhao, and Sankaran Mahadevan. "Variance Reduction Estimation in Bayesian Inference." In 19th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1772.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Kato, Shigeru, Naoki Wada, and Tomomichi Kagawa. "Food texture estimation by fuzzy inference." In 2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2017. http://dx.doi.org/10.1109/ifuzzy.2017.8311792.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Shen, Xuan, Geng Yuan, Wei Niu, Xiaolong Ma, Jiexiong Guan, Zhengang Li, Bin Ren, and Yanzhi Wang. "Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/715.

Full text

Abstract:

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.

APA, Harvard, Vancouver, ISO, and other styles

9

Li, Chengliu, John D. Fernstrom, Robert J. Sclabassi, Madelyn H. Fernstrom, Wenyan Jia, and Mingui Sun. "Food density estimation using fuzzy logic inference." In 2010 36th Annual Northeast Bioengineering Conference. IEEE, 2010. http://dx.doi.org/10.1109/nebc.2010.5458195.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Chen, Shuangshuang, Sihao Ding, L. Srikar Muppirisetty, Yiannis Karayiannidis, and Marten Bjorkman. "Amortized Variational Inference for Road Friction Estimation." In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020. http://dx.doi.org/10.1109/iv47402.2020.9304712.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Estimation and inference"

1

Collins, Joseph C. Quantal Response: Estimation and Inference. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada611092.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Rosen, Adam, Sokbae (Simon) Lee, and Victor Chernozhukov. Intersection Bounds: estimation and inference. The IFS, July 2009. http://dx.doi.org/10.1920/wp.cem.2009.1909.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Rosen, Adam, Sokbae (Simon) Lee, and Victor Chernozhukov. Intersection bounds: estimation and inference. Institute for Fiscal Studies, November 2011. http://dx.doi.org/10.1920/wp.cem.2011.3411.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Chernozhukov, Victor, Sokbae (Simon) Lee, and Adam Rosen. Intersection bounds: estimation and inference. Cemmap, October 2012. http://dx.doi.org/10.1920/wp.cem.2012.3312.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

McCloskey, Adam, Toru Kitagawa, and Isaiah Andrews. Inference after estimation of breaks. The IFS, October 2019. http://dx.doi.org/10.1920/wp.cem.2019.5119.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

McCloskey, Adam, Toru Kitagawa, and Isaiah Andrews. Inference after Estimation of Breaks. The IFS, July 2020. http://dx.doi.org/10.1920/wp.cem.2020.3420.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Lee, Sokbae (Simon), and Joel L. Horowitz. Nonparametric estimation and inference under shape restrictions. Cemmap, October 2015. http://dx.doi.org/10.1920/wp.cem.2015.6715.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Horowitz, Joel L., and Sokbae (Simon) Lee. Nonparametric estimation and inference under shape restrictions. IFS, July 2016. http://dx.doi.org/10.1920/wp.cem.2016.2916.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Horowitz, Joel L. Non-asymptotic inference in instrumental variables estimation. The IFS, October 2017. http://dx.doi.org/10.1920/wp.cem.2017.4617.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Horowitz, Joel L. Non-asymptotic inference in instrumental variables estimation. The IFS, September 2018. http://dx.doi.org/10.1920/wp.cem.2018.5218.

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!