WHAT DID FISHER MEAN by an ESTIMATE? 3 Ideas but Is in Conflict with His Ideology of Statistical Inference
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Confidence Interval Estimation in System Dynamics Models: Bootstrapping Vs
Confidence Interval Estimation in System Dynamics Models: Bootstrapping vs. Likelihood Ratio Method Gokhan Dogan* MIT Sloan School of Management, 30 Wadsworth Street, E53-364, Cambridge, Massachusetts 02142 [email protected] Abstract In this paper we discuss confidence interval estimation for system dynamics models. Confidence interval estimation is important because without confidence intervals, we cannot determine whether an estimated parameter value is significantly different from 0 or any other value, and therefore we cannot determine how much confidence to place in the estimate. We compare two methods for confidence interval estimation. The first, the “likelihood ratio method,” is based on maximum likelihood estimation. This method has been used in the system dynamics literature and is built in to some popular software packages. It is computationally efficient but requires strong assumptions about the model and data. These assumptions are frequently violated by the autocorrelation, endogeneity of explanatory variables and heteroskedasticity properties of dynamic models. The second method is called “bootstrapping.” Bootstrapping requires more computation but does not impose strong assumptions on the model or data. We describe the methods and illustrate them with a series of applications from actual modeling projects. Considering the empirical results presented in the paper and the fact that the bootstrapping method requires less assumptions, we suggest that bootstrapping is a better tool for confidence interval estimation in system dynamics -
A Framework for Sample Efficient Interval Estimation with Control
A Framework for Sample Efficient Interval Estimation with Control Variates Shengjia Zhao Christopher Yeh Stefano Ermon Stanford University Stanford University Stanford University Abstract timates, e.g. for each individual district or county, meaning that we need to draw a sufficient number of We consider the problem of estimating confi- samples for every district or county. Another diffi- dence intervals for the mean of a random vari- culty can arise when we need high accuracy (i.e. a able, where the goal is to produce the smallest small confidence interval), since confidence intervals possible interval for a given number of sam- producedp by typical concentration inequalities have size ples. While minimax optimal algorithms are O(1= number of samples). In other words, to reduce known for this problem in the general case, the size of a confidence interval by a factor of 10, we improved performance is possible under addi- need 100 times more samples. tional assumptions. In particular, we design This dilemma is generally unavoidable because concen- an estimation algorithm to take advantage of tration inequalities such as Chernoff or Chebychev are side information in the form of a control vari- minimax optimal: there exist distributions for which ate, leveraging order statistics. Under certain these inequalities cannot be improved. No-free-lunch conditions on the quality of the control vari- results (Van der Vaart, 2000) imply that any alter- ates, we show improved asymptotic efficiency native estimation algorithm that performs better (i.e. compared to existing estimation algorithms. outputs a confidence interval with smaller size) on some Empirically, we demonstrate superior perfor- problems, must perform worse on other problems. -
Likelihood Confidence Intervals When Only Ranges Are Available
Article Likelihood Confidence Intervals When Only Ranges are Available Szilárd Nemes Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg 405 30, Sweden; [email protected] Received: 22 December 2018; Accepted: 3 February 2019; Published: 6 February 2019 Abstract: Research papers represent an important and rich source of comparative data. The change is to extract the information of interest. Herein, we look at the possibilities to construct confidence intervals for sample averages when only ranges are available with maximum likelihood estimation with order statistics (MLEOS). Using Monte Carlo simulation, we looked at the confidence interval coverage characteristics for likelihood ratio and Wald-type approximate 95% confidence intervals. We saw indication that the likelihood ratio interval had better coverage and narrower intervals. For single parameter distributions, MLEOS is directly applicable. For location-scale distribution is recommended that the variance (or combination of it) to be estimated using standard formulas and used as a plug-in. Keywords: range, likelihood, order statistics, coverage 1. Introduction One of the tasks statisticians face is extracting and possibly inferring biologically/clinically relevant information from published papers. This aspect of applied statistics is well developed, and one can choose to form many easy to use and performant algorithms that aid problem solving. Often, these algorithms aim to aid statisticians/practitioners to extract variability of different measures or biomarkers that is needed for power calculation and research design [1,2]. While these algorithms are efficient and easy to use, they mostly are not probabilistic in nature, thus they do not offer means for statistical inference. Yet another field of applied statistics that aims to help practitioners in extracting relevant information when only partial data is available propose a probabilistic approach with order statistics. -
Statistical Theory
Statistical Theory Prof. Gesine Reinert November 23, 2009 Aim: To review and extend the main ideas in Statistical Inference, both from a frequentist viewpoint and from a Bayesian viewpoint. This course serves not only as background to other courses, but also it will provide a basis for developing novel inference methods when faced with a new situation which includes uncertainty. Inference here includes estimating parameters and testing hypotheses. Overview • Part 1: Frequentist Statistics { Chapter 1: Likelihood, sufficiency and ancillarity. The Factoriza- tion Theorem. Exponential family models. { Chapter 2: Point estimation. When is an estimator a good estima- tor? Covering bias and variance, information, efficiency. Methods of estimation: Maximum likelihood estimation, nuisance parame- ters and profile likelihood; method of moments estimation. Bias and variance approximations via the delta method. { Chapter 3: Hypothesis testing. Pure significance tests, signifi- cance level. Simple hypotheses, Neyman-Pearson Lemma. Tests for composite hypotheses. Sample size calculation. Uniformly most powerful tests, Wald tests, score tests, generalised likelihood ratio tests. Multiple tests, combining independent tests. { Chapter 4: Interval estimation. Confidence sets and their con- nection with hypothesis tests. Approximate confidence intervals. Prediction sets. { Chapter 5: Asymptotic theory. Consistency. Asymptotic nor- mality of maximum likelihood estimates, score tests. Chi-square approximation for generalised likelihood ratio tests. Likelihood confidence regions. Pseudo-likelihood tests. • Part 2: Bayesian Statistics { Chapter 6: Background. Interpretations of probability; the Bayesian paradigm: prior distribution, posterior distribution, predictive distribution, credible intervals. Nuisance parameters are easy. 1 { Chapter 7: Bayesian models. Sufficiency, exchangeability. De Finetti's Theorem and its intepretation in Bayesian statistics. { Chapter 8: Prior distributions. Conjugate priors. -
Estimation Methods in Multilevel Regression
Newsom Psy 526/626 Multilevel Regression, Spring 2019 1 Multilevel Regression Estimation Methods for Continuous Dependent Variables General Concepts of Maximum Likelihood Estimation The most commonly used estimation methods for multilevel regression are maximum likelihood-based. Maximum likelihood estimation (ML) is a method developed by R.A.Fisher (1950) for finding the best estimate of a population parameter from sample data (see Eliason,1993, for an accessible introduction). In statistical terms, the method maximizes the joint probability density function (pdf) with respect to some distribution. With independent observations, the joint probability of the distribution is a product function of the individual probabilities of events, so ML finds the likelihood of the collection of observations from the sample. In other words, it computes the estimate of the population parameter value that is the optimal fit to the observed data. ML has a number of preferred statistical properties, including asymptotic consistency (approaches the parameter value with increasing sample size), efficiency (lower variance than other estimators), and parameterization invariance (estimates do not change when measurements or parameters are transformed in allowable ways). Distributional assumptions are necessary, however, and there are potential biases in significance tests when using ML. ML can be seen as a more general method that encompasses ordinary least squares (OLS), where sample estimates of the population mean and regression parameters are equivalent for the two methods under regular conditions. ML is applied more broadly across statistical applications, including categorical data analysis, logistic regression, and structural equation modeling. Iterative Process For more complex problems, ML is an iterative process [for multilevel regression, usually Expectation- Maximization (EM) or iterative generalized least squares (IGLS) is used] in which initial (or “starting”) values are used first. -
32. Statistics 1 32
32. Statistics 1 32. STATISTICS Revised September 2007 by G. Cowan (RHUL). This chapter gives an overview of statistical methods used in High Energy Physics. In statistics, we are interested in using a given sample of data to make inferences about a probabilistic model, e.g., to assess the model’s validity or to determine the values of its parameters. There are two main approaches to statistical inference, which we may call frequentist and Bayesian. In frequentist statistics, probability is interpreted as the frequency of the outcome of a repeatable experiment. The most important tools in this framework are parameter estimation, covered in Section 32.1, and statistical tests, discussed in Section 32.2. Frequentist confidence intervals, which are constructed so as to cover the true value of a parameter with a specified probability, are treated in Section 32.3.2. Note that in frequentist statistics one does not define a probability for a hypothesis or for a parameter. Frequentist statistics provides the usual tools for reporting objectively the outcome of an experiment without needing to incorporate prior beliefs concerning the parameter being measured or the theory being tested. As such they are used for reporting most measurements and their statistical uncertainties in High Energy Physics. In Bayesian statistics, the interpretation of probability is more general and includes degree of belief (called subjective probability). One can then speak of a probability density function (p.d.f.) for a parameter, which expresses one’s state of knowledge about where its true value lies. Bayesian methods allow for a natural way to input additional information, such as physical boundaries and subjective information; in fact they require as input the prior p.d.f. -
Interval Estimation Statistics (OA3102)
Module 5: Interval Estimation Statistics (OA3102) Professor Ron Fricker Naval Postgraduate School Monterey, California Reading assignment: WM&S chapter 8.5-8.9 Revision: 1-12 1 Goals for this Module • Interval estimation – i.e., confidence intervals – Terminology – Pivotal method for creating confidence intervals • Types of intervals – Large-sample confidence intervals – One-sided vs. two-sided intervals – Small-sample confidence intervals for the mean, differences in two means – Confidence interval for the variance • Sample size calculations Revision: 1-12 2 Interval Estimation • Instead of estimating a parameter with a single number, estimate it with an interval • Ideally, interval will have two properties: – It will contain the target parameter q – It will be relatively narrow • But, as we will see, since interval endpoints are a function of the data, – They will be variable – So we cannot be sure q will fall in the interval Revision: 1-12 3 Objective for Interval Estimation • So, we can’t be sure that the interval contains q, but we will be able to calculate the probability the interval contains q • Interval estimation objective: Find an interval estimator capable of generating narrow intervals with a high probability of enclosing q Revision: 1-12 4 Why Interval Estimation? • As before, we want to use a sample to infer something about a larger population • However, samples are variable – We’d get different values with each new sample – So our point estimates are variable • Point estimates do not give any information about how far -
Confidence Intervals in Analysis and Reporting of Clinical Trials Guangbin Peng, Eli Lilly and Company, Indianapolis, IN
Confidence Intervals in Analysis and Reporting of Clinical Trials Guangbin Peng, Eli Lilly and Company, Indianapolis, IN ABSTRACT for more frequent use of confidence Regulatory agencies around the world intervals (Simon, 1993). have recommended reporting confidence There is a close relationship between intervals for treatment differences along confidence intervals and significance with the results of significance tests. tests (Hahn and Meeker, 1991); in fact, a SAS provides easy and convenient ways confidence interval can often be used to to produce confidence intervals using test a hypothesis. If the 100(1-α)% procedures such as PROC GLM and confidence interval for the mean PROC UNIVARIATE in conjunction treatment difference in a clinical trial with ODS (output delivery system). In does not contain zero, there is evidence this paper, I will discuss the relationship to indicate a treatment difference at the between significance tests and 100 α % significance level. This strategy confidence intervals, summarize the is equivalent to the hypothesis test that types of confidence intervals used in rejects the null hypothesis of no mean clinical study reports, and provide treatment difference at the level of α. examples from clinical trials to illustrate Compared to p-values, confidence the computation of distribution- intervals are generally more informative. dependent confidence intervals for the They provide quantitative bounds that mean treatment difference and express the uncertainty inherent in distribution-free confidence intervals for estimation, instead of merely an accept the median response within each or reject statement. The length of a treatment group using SAS. confidence interval depends on the sample size; this influence of sample INTRODUCTION size is evident from observing the length Confidence interval estimation and of the interval, while this is not the case significance testing (hypothesis testing) for a significance test. -
Akaike's Information Criterion
ESCI 340 Biostatistical Analysis Model Selection with Information Theory "Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise." − John W. Tukey, (1962), "The future of data analysis." Annals of Mathematical Statistics 33, 1-67. 1 Problems with Statistical Hypothesis Testing 1.1 Indirect approach: − effort to reject null hypothesis (H0) believed to be false a priori (statistical hypotheses are not the same as scientific hypotheses) 1.2 Cannot accommodate multiple hypotheses (e.g., Chamberlin 1890) 1.3 Significance level (α) is arbitrary − will obtain "significant" result if n large enough 1.4 Tendency to focus on P-values rather than magnitude of effects 2 Practical Alternative: Direct Evaluation of Multiple Hypotheses 2.1 General Approach: 2.1.1 Develop multiple hypotheses to answer research question. 2.1.2 Translate each hypothesis into a model. 2.1.3 Fit each model to the data (using least squares, maximum likelihood, etc.). (fitting model ≅ estimating parameters) 2.1.4 Evaluate each model using information criterion (e.g., AIC). 2.1.5 Select model that performs best, and determine its likelihood. 2.2 Model Selection Criterion 2.2.1 Akaike Information Criterion (AIC): relates information theory to maximum likelihood ˆ AIC = −2loge[L(θ | data)]+ 2K θˆ = estimated model parameters ˆ loge[L(θ | data)] = log-likelihood, maximized over all θ K = number of parameters in model Select model that minimizes AIC. 2.2.2 Modification for complex models (K large relative to sample size, n): 2K(K +1) AIC = −2log [L(θˆ | data)]+ 2K + c e n − K −1 use AICc when n/K < 40. -
Plausibility Functions and Exact Frequentist Inference
Plausibility functions and exact frequentist inference Ryan Martin Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago [email protected] July 6, 2018 Abstract In the frequentist program, inferential methods with exact control on error rates are a primary focus. The standard approach, however, is to rely on asymptotic ap- proximations, which may not be suitable. This paper presents a general framework for the construction of exact frequentist procedures based on plausibility functions. It is shown that the plausibility function-based tests and confidence regions have the desired frequentist properties in finite samples—no large-sample justification needed. An extension of the proposed method is also given for problems involving nuisance parameters. Examples demonstrate that the plausibility function-based method is both exact and efficient in a wide variety of problems. Keywords and phrases: Bootstrap; confidence region; hypothesis test; likeli- hood; Monte Carlo; p-value; profile likelihood. 1 Introduction In the Neyman–Pearson program, construction of tests or confidence regions having con- trol over frequentist error rates is an important problem. But, despite its importance, there seems to be no general strategy for constructing exact inferential methods. When an exact pivotal quantity is not available, the usual strategy is to select some summary statistic and derive a procedure based on the statistic’s asymptotic sampling distribu- tion. First-order methods, such as confidence regions based on asymptotic normality arXiv:1203.6665v4 [math.ST] 12 Mar 2014 of the maximum likelihood estimator, are known to be inaccurate in certain problems. Procedures with higher-order accuracy are also available (e.g., Brazzale et al. -
Interval Estimation for Drop-The-Losers Designs
Biometrika (2010), 97,2,pp. 405–418 doi: 10.1093/biomet/asq003 C 2010 Biometrika Trust Advance Access publication 14 April 2010 Printed in Great Britain Interval estimation for drop-the-losers designs BY SAMUEL S. WU Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida 32610, U.S.A. [email protected]fl.edu WEIZHEN WANG Downloaded from Department of Mathematics and Statistics, Wright State University, Dayton, Ohio 45435, U.S.A. [email protected] AND MARK C. K. YANG Department of Statistics, University of Florida, Gainesville, Florida 32610, U.S.A. http://biomet.oxfordjournals.org [email protected]fl.edu SUMMARY In the first stage of a two-stage, drop-the-losers design, a candidate for the best treatment is selected. At the second stage, additional observations are collected to decide whether the candidate is actually better than the control. The design also allows the investigator to stop the trial for ethical reasons at the end of the first stage if there is already strong evidence of futility or superiority. Two types of tests have recently been developed, one based on the combined means and the other based on the combined p-values, but corresponding inter- at University of Florida on May 27, 2010 val estimators are unavailable except in special cases. The problem is that, in most cases, the interval estimators depend on the mean configuration of all treatments in the first stage, which is unknown. In this paper, we prove a basic stochastic ordering lemma that enables us to bridge the gap between hypothesis testing and interval estimation. -
An Introduction to Maximum Likelihood in R
1 INTRODUCTION 1 An introduction to Maximum Likelihood in R Stephen P. Ellner ([email protected]) Department of Ecology and Evolutionary Biology, Cornell University Last compile: June 3, 2010 1 Introduction Maximum likelihood as a general approach to estimation and inference was created by R. A. Fisher between 1912 and 1922, starting with a paper written as a third-year undergraduate. Then, and for many years, it was more of theoretical than practical interest. Now, the ability to do nonlinear optimization on the computer has made likelihood methods practical and very popular. Let’s start with the probability density function for one observation x from normal random variable with mean µ and variance σ2, (x µ)2 1 − − f(x µ, σ)= e 2σ2 . (1) | √2πσ For a set of n replicate independent observations x1,x2, ,xn the joint density is ··· n f(x1,x2, ,xn µ, σ)= f(xi µ, σ) (2) ··· | i=1 | Y We interpret this as follows: given the values of µ and σ, equation (2) tells us the relative probability of different possible values of the observations. Maximum likelihood turns this around by defining the likelihood function n (µ, σ x1,x2, ,xn)= f(xi µ, σ) (3) L | ··· i=1 | Y The right-hand side is the same as (2) but the interpretation is different: given the observations, the function tells us the relative likelihood of different possible values of the parameters for L the process that generated the data. Note: the likelihood function is not a probability, and it does not specifying the relative probability of different parameter values.