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Journal articles on the topic 'Estimation and inference'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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11

Chao, John C., and Peter C. B. Phillips. "Uniform Inference in Panel Autoregression." Econometrics 7, no. 4 (November 26, 2019): 45. http://dx.doi.org/10.3390/econometrics7040045.

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This paper considers estimation and inference concerning the autoregressive coefficient ( ρ ) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for ρ that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson–Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when ρ is near unity. But it is not so well known that AH IV estimation is still consistent when ρ = 1 . In fact, the AH estimating equation is very well-centered and is an unbiased estimating equation in the sense of Durbin (1960), a feature that is especially useful in confidence interval construction. We show that a properly normalized statistic based on the AH estimating equation, which we call the M statistic, is uniformly convergent and can be inverted to obtain asymptotically valid interval estimates. To further improve the informativeness of our confidence procedure in the unit root and near unit root regions and to alleviate the problem that the AH procedure has greater variation in these regions, we use information from unit root pretesting to select among alternative confidence intervals. Two sequential tests are used to assess how close ρ is to unity, and different intervals are applied depending on whether the test results indicate ρ to be near or far away from unity. When ρ is relatively close to unity, our procedure activates intervals whose width shrinks to zero at a faster rate than that of the confidence interval based on the M statistic. Only when both of our unit root tests reject the null hypothesis does our procedure turn to the M statistic interval, whose width has the optimal N - 1 / 2 T - 1 / 2 rate of shrinkage when the underlying process is stable. Our asymptotic analysis shows this pretest-based confidence procedure to have coverage probability that is at least the nominal level in large samples uniformly over the parameter space. Simulations confirm that the proposed interval estimation methods perform well in finite samples and are easy to implement in practice. A supplement to the paper provides an extensive set of new results on the asymptotic behavior of panel IV estimators in weak instrument settings.

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12

Ding, Feilong, Cheng Chi, Yu Li, and Haining Huang. "Variational Bayesian Inference Time Delay Estimation for Passive Sonars." Journal of Marine Science and Engineering 11, no. 1 (January 12, 2023): 194. http://dx.doi.org/10.3390/jmse11010194.

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In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sampling rate). When a true time delay is out of the grid, the estimation accuracy deteriorates due to the mismatch between the real-time delay and the discrete grid. This paper proposes a new method for time delay estimation, which realizes the time delay estimation under the framework of variational Bayesian inference. The proposed method is grid-less, that is, continuous in the time domain. Unlike the popular grid-less compressive time delay estimation method, this method does not require parameter adjustment, and can automatically estimate the number of time delays, noise variance, and amplitude variance. The simulation results showed that the performance of the proposed method was superior to the reference state-of-the-art time delay estimation methods.

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13

Verdier, Valentin. "Estimation and Inference for Linear Models with Two-Way Fixed Effects and Sparsely Matched Data." Review of Economics and Statistics 102, no. 1 (March 2020): 1–16. http://dx.doi.org/10.1162/rest_a_00807.

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Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are sequentially exogenous (e.g., dynamic models), and I propose a new estimator for these models. Finally, I illustrate these methods by providing estimates of the effect of class size reductions on student achievement.

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14

Chen, Ziqi, Kirill V. Horoshenkov, and Ning Xiang. "Bayesian inference for boundary admittance estimation using a multipole model for room-acoustic simulation." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A348. http://dx.doi.org/10.1121/10.0008540.

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Acoustic surface admittance/impedance at room boundaries is essential for wave-based room-acoustic simulations. In this work, two levels of Bayesian inference are applied to estimate the surface admittance based on a multipole admittance model. This work estimates the order of the multipole admittance model through the high level of inference, Bayesian model selection. The first (low) level of inference, Bayesian parameter estimation, is applied to estimate the parameter values of the surface admittance model once model order is selected. This work approximates the frequency-dependent admittance from experimentally measured a set of acoustic surface admittance data. Analysis results demonstrate that multipole model-based Bayesian inference is well suited in estimating the frequency-dependent boundary condition within wave-based simulation framework. Numerical simulations verify the estimation results of Bayesian inference.

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15

Jean-Marie-Dufour, Russell Davidson, and MacKinnon. "Estimation and Inference in Econometrics." Canadian Journal of Economics 28, no. 3 (August 1995): 718. http://dx.doi.org/10.2307/136059.

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16

Davidson, James, Russell Davidson, and James G. MacKinnon. "Estimation and Inference in Econometrics." Economica 62, no. 245 (February 1995): 133. http://dx.doi.org/10.2307/2554780.

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17

Smith, Richard J., and H. White. "Estimation, Inference and Specification Analysis." Economica 63, no. 251 (August 1996): 522. http://dx.doi.org/10.2307/2555022.

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18

Levin, Andrew, Russell Davidson, and James G. MacKinnon. "Estimation and Inference in Econometrics." Journal of the American Statistical Association 89, no. 427 (September 1994): 1143. http://dx.doi.org/10.2307/2290953.

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19

Bierens, Herman J., and Halbert White. "Estimation, Inference and Specification Analysis." Journal of the American Statistical Association 91, no. 435 (September 1996): 1373. http://dx.doi.org/10.2307/2291755.

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20

Chambers, Marcus J., Russell Davidson, and James G. MacKinnon. "Estimation and Inference in Econometrics." Economic Journal 104, no. 424 (May 1994): 703. http://dx.doi.org/10.2307/2234656.

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21

Pagan, Adrian, and Halbert White. "Estimation, Inference and Specification Analysis." Economic Journal 106, no. 438 (September 1996): 1444. http://dx.doi.org/10.2307/2235549.

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22

Belotti, Federico, Edoardo Di Porto, and Gianluca Santoni. "Spatial Differencing: Estimation and Inference." CESifo Economic Studies 64, no. 2 (February 28, 2018): 241–54. http://dx.doi.org/10.1093/cesifo/ify003.

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23

Harris, David, Brendan McCabe, and Stephen Leybourne. "Stochastic cointegration: estimation and inference." Journal of Econometrics 111, no. 2 (December 2002): 363–84. http://dx.doi.org/10.1016/s0304-4076(02)00111-2.

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24

Xu, Ke-Li. "REWEIGHTED FUNCTIONAL ESTIMATION OF DIFFUSION MODELS." Econometric Theory 26, no. 2 (September 30, 2009): 541–63. http://dx.doi.org/10.1017/s0266466609100087.

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The local linear method is popular in estimating nonparametric continuous-time diffusion models, but it may produce negative results for the diffusion (or volatility) functions and thus lead to insensible inference. We demonstrate this using U.S. interest rate data. We propose a new functional estimation method of the diffusion coefficient based on reweighting the conventional Nadaraya–Watson estimator. It preserves the appealing bias properties of the local linear estimator and is guaranteed to be nonnegative in finite samples. A limit theory is developed under mild requirements (recurrence) of the data generating mechanism without assuming stationarity or ergodicity.

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25

Zhang, Yichong, and Xin Zheng. "Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization." Quantitative Economics 11, no. 3 (2020): 957–82. http://dx.doi.org/10.3982/qe1323.

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In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.

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26

Li, Zhang Miao, Xin Jian Kou, and Jian Yong Huang. "The Interval Estimation about Confidence of the Reliability Index." Applied Mechanics and Materials 166-169 (May 2012): 1854–58. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1854.

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The theory of statistical inferences is the base of the reliability analysis. The results of inferences can not be believed unquestionably because the data sample could not involve whole information about the parent distribution. In engineering, it is reasonable to estimate the precision of inference results concerning the reliability index. Therefore, the randomness of reliability index is discussed in this paper. The confidence interval, which is one of the most important concepts in statistics, is employed to express the precision of the reliability index.

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27

Bollerslev, Tim, Jia Li, and Zhipeng Liao. "Fixed‐ k inference for volatility." Quantitative Economics 12, no. 4 (2021): 1053–84. http://dx.doi.org/10.3982/qe1749.

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We present a new theory for the conduct of nonparametric inference about the latent spot volatility of a semimartingale asset price process. In contrast to existing theories based on the asymptotic notion of an increasing number of observations in local estimation blocks, our theory treats the estimation block size k as fixed. While the resulting spot volatility estimator is no longer consistent, the new theory permits the construction of asymptotically valid and easy‐to‐calculate pointwise confidence intervals for the volatility at any given point in time. Extending the theory to a high‐dimensional inference setting with a growing number of estimation blocks further permits the construction of uniform confidence bands for the volatility path. An empirically realistically calibrated simulation study underscores the practical reliability of the new inference procedures. An empirical application based on intraday data for the S&P 500 equity index reveals highly significant abrupt changes, or jumps, in the market volatility at FOMC news announcement times, validating recent uses of various high‐frequency‐based identification schemes in asset pricing finance and monetary economics.

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Meselidis, Christos, and Alex Karagrigoriou. "Contingency Table Analysis and Inference via Double Index Measures." Entropy 24, no. 4 (March 29, 2022): 477. http://dx.doi.org/10.3390/e24040477.

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In this work, we focus on a general family of measures of divergence for estimation and testing with emphasis on conditional independence in cross tabulations. For this purpose, a restricted minimum divergence estimator is used for the estimation of parameters under constraints and a new double index (dual) divergence test statistic is introduced and thoroughly examined. The associated asymptotic theory is provided and the advantages and practical implications are explored via simulation studies.

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Liu, Jie, Wenqian Dong, Qingqing Zhou, and Dong Li. "Fauce." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 1950–63. http://dx.doi.org/10.14778/3476249.3476254.

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Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

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Zhu, Hai Ting, Wei Ding, and Jun Hui Ni. "A New Virus Source Inference Model Based on Network Performance Estimation." Applied Mechanics and Materials 433-435 (October 2013): 1693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1693.

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Combining network tomography, a new virus source inference model based on network performance is proposed. Both the topology information and real time network status are considered in our model. Performance metrics are introduced into rumor-centrality-based source detecting algorithm in an active inference way. By improving the process of setting up the spanning tree of infected topology we raise the virus source inference precision and reduce the time complexity of rumor-centrality-based algorithm from O(N2(|V|+|E|)) to O(N2). The simulation results show that our model achieves better estimation accuracy than the algorithm using rumor center as the estimator.

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Hessami, Masoud, François Anctil, and Alain A. Viau. "An adaptive neuro-fuzzy inference system for the post-calibration of weather radar rainfall estimation." Journal of Hydroinformatics 5, no. 1 (January 1, 2003): 63–70. http://dx.doi.org/10.2166/hydro.2003.0005.

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An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membership functions for the fuzzy inference system. Fuzzy logic enhances the generalisation of the artificial neural network system. In order to demonstrate the steps of the radar rainfall post-calibration using the Adaptive Neuro-Fuzzy Inference System, CAPPIs of one-hour rainfall accumulation and corresponding raingauge observations have been selected. Results show that the proposed approach looks for a response that is a compromise between radar rainfall estimations and raingauge observations and does not necessarily consider the raingauge observations as ground truth. The algorithm is very fast and can be implemented for real time post-calibration. This algorithm makes use of all available data—raingauge observations are usually scarce—for training and checking the neuro-fuzzy inference system. It also provides a degree of reliability of the post-calibration.

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Baba, Takamichi, Takayuki Kanemori, and Yoshiyuki Ninomiya. "A $C_p$ criterion for semiparametric causal inference." Biometrika 104, no. 4 (October 9, 2017): 845–61. http://dx.doi.org/10.1093/biomet/asx054.

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Summary For marginal structural models, which play an important role in causal inference, we consider a model selection problem within a semiparametric framework using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modelling target is a potential outcome that may be missing, so there is no classical information criterion. We define a mean squared error for treating the potential outcome and derive an asymptotic unbiased estimator as a $C_{p}$ criterion using an ignorable treatment assignment condition. Simulation shows that the proposed criterion outperforms a conventional one by providing smaller squared errors and higher frequencies of selecting the true model in all the settings considered. Moreover, in a real-data analysis we found a clear difference between the two criteria.

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Koch, Brandon, David M. Vock, Julian Wolfson, and Laura Boehm Vock. "Variable selection and estimation in causal inference using Bayesian spike and slab priors." Statistical Methods in Medical Research 29, no. 9 (January 15, 2020): 2445–69. http://dx.doi.org/10.1177/0962280219898497.

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Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Standard variable selection techniques aim to maximize predictive ability of the outcome model, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. We propose a novel method for estimating causal effects that simultaneously considers models for both outcome and treatment, which we call the bilevel spike and slab causal estimator (BSSCE). By using a Bayesian formulation, BSSCE estimates the posterior distribution of all model parameters and provides straightforward and reliable inference. Spike and slab priors are used on each covariate coefficient which aim to minimize the mean squared error of the treatment effect estimator. Theoretical properties of the treatment effect estimator are derived justifying the prior used in BSSCE. Simulations show that BSSCE can substantially reduce mean squared error over numerous methods and performs especially well with large numbers of covariates, including situations where the number of covariates is greater than the sample size. We illustrate BSSCE by estimating the causal effect of vasoactive therapy vs. fluid resuscitation on hypotensive episode length for patients in the Multiparameter Intelligent Monitoring in Intensive Care III critical care database.

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Ichikawa, Kohei, and Asaki Kataoka. "Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes." Neural Computation 34, no. 3 (February 17, 2022): 804–27. http://dx.doi.org/10.1162/neco_a_01477.

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Abstract Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.

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Bao, Yong, Xiaotian Liu, and Lihong Yang. "Indirect Inference Estimation of Spatial Autoregressions." Econometrics 8, no. 3 (September 3, 2020): 34. http://dx.doi.org/10.3390/econometrics8030034.

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The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

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36

Müller, Peter, Brani Vidakovic, and Peter Muller. "Bayesian Inference with Wavelets: Density Estimation." Journal of Computational and Graphical Statistics 7, no. 4 (December 1998): 456. http://dx.doi.org/10.2307/1390676.

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37

Sen, P. K., N. Balakrishnan, and A. Clifford Cohen. "Order Statistics and Inference: Estimation Methods." Journal of the American Statistical Association 87, no. 419 (September 1992): 909. http://dx.doi.org/10.2307/2290249.

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38

Newson, R., N. Balakrishnan, and A. C. Cohen. "Order Statistics and Inference: Estimation Methods." Biometrics 48, no. 2 (June 1992): 657. http://dx.doi.org/10.2307/2532322.

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39

de Winkel, Ksander N., Mikhail Katliar, and Heinrich H. Bülthoff. "Causal Inference in Multisensory Heading Estimation." PLOS ONE 12, no. 1 (January 6, 2017): e0169676. http://dx.doi.org/10.1371/journal.pone.0169676.

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40

Bai, Jushan, Kunpeng Li, and Lina Lu. "Estimation and Inference of FAVAR Models." Journal of Business & Economic Statistics 34, no. 4 (September 15, 2016): 620–41. http://dx.doi.org/10.1080/07350015.2015.1111222.

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41

Fan, J., M. Farmen, and I. Gijbels. "Local maximum likelihood estimation and inference." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60, no. 3 (August 1998): 591–608. http://dx.doi.org/10.1111/1467-9868.00142.

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42

Burr, Thomas L. "Bayesian Inference: Parameter Estimation and Decisions." Technometrics 46, no. 2 (May 2004): 250–51. http://dx.doi.org/10.1198/tech.2004.s793.

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43

Calin-Jageman, Robert J., and Geoff Cumming. "Estimation for Better Inference in Neuroscience." eneuro 6, no. 4 (July 2019): ENEURO.0205–19.2019. http://dx.doi.org/10.1523/eneuro.0205-19.2019.

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44

Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Ernst Schaumburg. "Characteristic-Sorted Portfolios: Estimation and Inference." Review of Economics and Statistics 102, no. 3 (June 2020): 531–51. http://dx.doi.org/10.1162/rest_a_00883.

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Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.

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45

Müller, Peter, and Brani Vidakovic. "Bayesian Inference with Wavelets: Density Estimation." Journal of Computational and Graphical Statistics 7, no. 4 (December 1998): 456–68. http://dx.doi.org/10.1080/10618600.1998.10474788.

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46

Bennett, Christopher J., and Shabana Mitra. "Multidimensional Poverty: Measurement, Estimation, and Inference." Econometric Reviews 32, no. 1 (January 2013): 57–83. http://dx.doi.org/10.1080/07474938.2012.690331.

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Du, Sizhen, Guojie Song, and Haikun Hong. "Collective causal inference with lag estimation." Neurocomputing 323 (January 2019): 299–310. http://dx.doi.org/10.1016/j.neucom.2018.09.088.

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van Akkeren, Marco, George Judge, and Ron Mittelhammer. "Generalized moment based estimation and inference." Journal of Econometrics 107, no. 1-2 (March 2002): 127–48. http://dx.doi.org/10.1016/s0304-4076(01)00116-6.

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Parmeter, Christopher F., Kai Sun, Daniel J. Henderson, and Subal C. Kumbhakar. "Estimation and inference under economic restrictions." Journal of Productivity Analysis 41, no. 1 (March 23, 2013): 111–29. http://dx.doi.org/10.1007/s11123-013-0339-x.

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Prescott, P., N. Balakrishnan, and A. C. Cohen. "Order Statistics and Inference--Estimation Methods." Journal of the Royal Statistical Society. Series A (Statistics in Society) 155, no. 2 (1992): 307. http://dx.doi.org/10.2307/2982964.

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