Correcting sample selection bias in maximum entropy density
... we require that unbiased models not use any knowledge of sample selection bias during testing. This requirement is vital for habitat modeling where models are often applied to a different region or under different climatic conditions. To our knowledge this is the first work addressing sample selecti ...
... we require that unbiased models not use any knowledge of sample selection bias during testing. This requirement is vital for habitat modeling where models are often applied to a different region or under different climatic conditions. To our knowledge this is the first work addressing sample selecti ...
Simulated Maximum Likelihood for Continuous
... flowing phenomenon, the empirical application is more difficult in comparison to time series models. This is in part due to the difficulty in computing likelihood functions for sampled, discrete time measurements (daily, weekly etc.), as they occur in empirical research. With large sampling interval ...
... flowing phenomenon, the empirical application is more difficult in comparison to time series models. This is in part due to the difficulty in computing likelihood functions for sampled, discrete time measurements (daily, weekly etc.), as they occur in empirical research. With large sampling interval ...
Probability Sampling
... The count X of successes in a binomial setting is a binomial random variable. The probability distribution of X is a binomial distribution with parameters n and p, where n is the number of trials of the chance process and p is the probability of a success on any one trial. The possible values of X a ...
... The count X of successes in a binomial setting is a binomial random variable. The probability distribution of X is a binomial distribution with parameters n and p, where n is the number of trials of the chance process and p is the probability of a success on any one trial. The possible values of X a ...
Point Estimation Module - Naval Postgraduate School
... • Tchebysheff’s Theorem. Let Y be a random variable with finite mean m and variance s2. Then for any k > 0, Pr Y m ks 1 1 k 2 – Note that this holds for any distribution – It is a (generally conservative) bound – E.g., for any distribution we’re guaranteed that the probability Y is withi ...
... • Tchebysheff’s Theorem. Let Y be a random variable with finite mean m and variance s2. Then for any k > 0, Pr Y m ks 1 1 k 2 – Note that this holds for any distribution – It is a (generally conservative) bound – E.g., for any distribution we’re guaranteed that the probability Y is withi ...
Chapter 4: Discrete Probability Distributions
... a.) The distance your car travels on a tank of gas The distance your car travels is a continuous random variable because it is a measurement that cannot be counted. (All measurements are continuous random variables.) b.) The number of students in a statistics class The number of students is a discre ...
... a.) The distance your car travels on a tank of gas The distance your car travels is a continuous random variable because it is a measurement that cannot be counted. (All measurements are continuous random variables.) b.) The number of students in a statistics class The number of students is a discre ...
Applied Statistics in Occupational Safety and Health
... samples and, if used correctly, can describe a population. This is the case with inferential statistics. Second, statistics can be defined as the science that deals with the collection, tabulation, and systematic classification of data. Statistics as a field of study uses mathematics and probability ...
... samples and, if used correctly, can describe a population. This is the case with inferential statistics. Second, statistics can be defined as the science that deals with the collection, tabulation, and systematic classification of data. Statistics as a field of study uses mathematics and probability ...
Fractured Spaghetti and Other Probability Topics
... then pick 12 and 34 as your numbers. Pick the first two numbers whose sum is less than 100, the length of your spaghetti. [Our purpose here is to choose a random sample of two numbers, i.e. a sample chosen so that each sample of size two is equally likely to be picked.] Do this ten times, i.e. for t ...
... then pick 12 and 34 as your numbers. Pick the first two numbers whose sum is less than 100, the length of your spaghetti. [Our purpose here is to choose a random sample of two numbers, i.e. a sample chosen so that each sample of size two is equally likely to be picked.] Do this ten times, i.e. for t ...
Introduction to statistical modelling - Statistics
... (taking values Male and Female), Male could be coded as 1, Female as 2. But this does not make Sex a numerical variable: the numbers 1 and 2 are just labels. The category Male could equally well be coded as 2 and the category Female as 1. A distinction is drawn between two different kinds of numerica ...
... (taking values Male and Female), Male could be coded as 1, Female as 2. But this does not make Sex a numerical variable: the numbers 1 and 2 are just labels. The category Male could equally well be coded as 2 and the category Female as 1. A distinction is drawn between two different kinds of numerica ...
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as ""all persons living in a country"" or ""every atom composing a crystal"". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a ""false positive"") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a ""false negative""). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.