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Class of statistical estimators
In statistics, M-estimators are a broad class of extremum estimators for which the objective function is a sample average. Both non-linear least squares
M-estimator
Mathematical decision rule
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value
Bayes_estimator
Unbiased statistical estimator minimizing variance
minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than
Minimum-variance unbiased estimator
Minimum-variance_unbiased_estimator
Type of statistics
L-estimators are a general class of simple statistics, often robust, while M-estimators are a general class of robust statistics, and are now the preferred solution
Robust_statistics
Middle quantile of a data set or probability distribution
Bayesian L 1 {\displaystyle L_{1}} estimator: m ( X | Y = y ) = arg min f E [ | X − f ( Y ) | ] {\displaystyle m(X|Y=y)=\arg \min _{f}\operatorname
Median
Two-step M-estimators deals with M-estimation problems that require preliminary estimation to obtain the parameter of interest. Two-step M-estimation
Two-step_M-estimator
Method of estimating the parameters of a statistical model, given observations
estimation M-estimator: an approach used in robust statistics Maximum a posteriori (MAP) estimator: for a contrast in the way to calculate estimators when prior
Maximum_likelihood_estimation
Non-parametric statistic used to estimate the survival function
The Kaplan–Meier estimator, also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime
Kaplan–Meier_estimator
In statistics, redescending M-estimators are Ψ-type M-estimators which have ψ functions that are non-decreasing near the origin, but decreasing toward
Redescending_M-estimator
Rule for calculating an estimate of a given quantity based on observed data
statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity
Estimator
Statistical property
In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter
Bias_of_an_estimator
Rule for estimating the mean of a dataset
James–Stein estimator is an estimator of the mean θ := ( θ 1 , θ 2 , … θ m ) {\displaystyle {\boldsymbol {\theta }}:=(\theta _{1},\theta _{2},\dots \theta _{m})}
James–Stein_estimator
Loss function used in robust regression
robust statistics, M-estimation and additive modelling. Winsorizing Robust regression M-estimator Visual comparison of different M-estimators Huber, Peter J
Huber_loss
Parameter estimation via sample statistics
generally, a point estimator can be contrasted with a set estimator. Examples are given by confidence sets or credible sets. A point estimator can also be contrasted
Point_estimation
Statistical estimator for ratio of means
The ratio estimator is a statistical estimator for the ratio of means of two random variables. Ratio estimates are biased and corrections must be made
Ratio_estimator
Method of estimating the parameters of a statistical model
see that the MAP estimator for μ is given by μ ^ M A P = σ m 2 n σ m 2 n + σ v 2 ( 1 n ∑ j = 1 n x j ) + σ v 2 σ m 2 n + σ v 2 μ 0 = σ m 2 ( ∑ j = 1 n x
Maximum a posteriori estimation
Maximum_a_posteriori_estimation
Statistical theorem
that characterizes the transformation of an arbitrarily crude estimator into an estimator that is optimal by the mean-squared-error criterion or any of
Rao–Blackwell_theorem
Measure of variation in statistics
standard deviation. Such a statistic is called an estimator, and the estimator (or the value of the estimator, namely the estimate) is called a sample standard
Standard_deviation
Statistical measure of association
described in the following section. Cramér's V can be a heavily biased estimator of its population counterpart and will tend to overestimate the strength
Cramér's_V
Concept in machine learning
Loog, Marco; Viering, Tom; Mey, Alexander; Krijthe, Jesse H.; Tax, David M. J. (2020-05-19). "A brief prehistory of double descent". Proceedings of the
Double_descent
Statistical measure of variability
small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better
Median_absolute_deviation
Distribution function associated with the empirical measure of a sample
that F ^ n ( t ) {\displaystyle {\widehat {F}}_{n}(t)} is an unbiased estimator for F(t). In some textbooks, the empirical distribution function is defined
Empirical distribution function
Empirical_distribution_function
Robust and nonparametric estimator of a population's location parameter
In statistics, the Hodges–Lehmann estimator is a robust and nonparametric estimator of a population's location parameter. For populations that are symmetric
Hodges–Lehmann_estimator
Measure of the shape of a function
if that moment exists, for any sample size n. It is thus an unbiased estimator. This contrasts with the situation for central moments, whose computation
Moment_(mathematics)
Fourth standardized moment in statistics
sample of n values, a method of moments estimator of the population excess kurtosis can be defined as g 2 ≡ m 4 m 2 2 − 3 = 1 n ∑ i = 1 n ( x i − x ¯ )
Kurtosis
Quality measure of a statistical method
of quality of an estimator, of an experimental design, or of a hypothesis testing procedure. Essentially, a more efficient estimator needs fewer input
Efficiency_(statistics)
Measure of linear correlation
\quad } therefore r is a biased estimator of ρ . {\displaystyle \rho .} The unique minimum variance unbiased estimator radj is given by where: r , n {\displaystyle
Pearson correlation coefficient
Pearson_correlation_coefficient
Mathematical relation assigning a probability event to a cost
median is the estimator that minimizes expected loss experienced under the absolute-difference loss function. Still different estimators would be optimal
Loss_function
Nonparametric estimate of cumulative hazard
The Nelson–Aalen estimator is a non-parametric estimator of the cumulative hazard rate function in case of censored data or incomplete data. It is used
Nelson–Aalen_estimator
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
{s}{\bar {x}}}} But this estimator, when applied to a small or moderately sized sample, tends to be too low: it is a biased estimator. For normally distributed
Coefficient_of_variation
Statistical measure of how far values spread from their average
unbiased estimator (dividing by a number larger than n − 1) and is a simple example of a shrinkage estimator: one "shrinks" the unbiased estimator towards
Variance
Statistical property
The standard error (SE) of a statistic (usually an estimator of a parameter, like the average or mean) is the standard deviation of its sampling distribution
Standard_error
Family of statistical methods based on sampling of available data
is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with
Resampling_(statistics)
Type of statistical measure over subsets of a dataset
{\text{Total}}_{M}} , then Total M + 1 = Total M + p M + 1 − p M − n + 1 Numerator M + 1 = Numerator M + n p M + 1 − Total M WMA M + 1 = Numerator M + 1 n + ( n − 1 )
Moving_average
Kth smallest value in a statistical sample
parameter for the order statistic based density estimator is the size of sample subsets. Such an estimator is more robust than histogram and kernel based
Order_statistic
Statistical model allowing for frequent zero values
moments estimators are given by λ ^ m o = s 2 + m 2 m − 1 , {\displaystyle {\hat {\lambda }}_{mo}={\frac {s^{2}+m^{2}}{m}}-1,} π ^ m o = s 2 − m s 2 + m 2 −
Zero-inflated_model
Experiment methodology
the mean of the variable to be optimized is the most common choice of estimator, others are regularly used. Fisher's exact test can be employed to compare
A/B_testing
Measure of the asymmetry of random variables
parameters For a sample of n values, two natural estimators of the population skewness are b 1 = m 3 s 3 = 1 n ∑ i = 1 n ( x i − x ¯ ) 3 [ 1 n − 1 ∑
Skewness
Statistical hypothesis test
uncorrelated). Let α ^ , β ^ = least-squares estimators , S E α ^ , S E β ^ = the standard errors of least-squares estimators . {\displaystyle {\begin{aligned}{\hat
Student's_t-test
Statistical transformation
Similarly expanding the mean m and variance v of artanh ( r ) {\displaystyle \operatorname {artanh} (r)} , one gets m = artanh ( ρ ) + ρ 2 N + O (
Fisher_transformation
Statistical method for resampling
the bootstrap. Given a sample of size n {\displaystyle n} , a jackknife estimator can be built by aggregating the parameter estimates from each subsample
Jackknife_resampling
Measure of statistical dispersion
75th percentile, so IQR = Q3 − Q1. The IQR is an example of a trimmed estimator, defined as the 25% trimmed range, which enhances the accuracy of dataset
Interquartile_range
Statistic for rank correlation
Theil–Sen estimator Mann–Whitney U test - it is equivalent to Kendall's tau correlation coefficient if one of the variables is binary. Kendall, M. G. (1938)
Kendall rank correlation coefficient
Kendall_rank_correlation_coefficient
Summary statistic of variability
{E} \left[|X-{\text{median}}|\right]} This is the maximum likelihood estimator of the scale parameter b {\displaystyle b} of the Laplace distribution
Average_absolute_deviation
Statistics concept
have a random sample of n people. The sample mean could serve as a good estimator of the population mean. Then we have: The difference between the height
Errors_and_residuals
Probability of survival beyond any specified time
model the survival function is the non-parametric Kaplan–Meier estimator. This estimator requires lifetime data. Periodic case (cohort) and death (and
Survival_function
Range to estimate an unknown parameter
Kiefer, J. (1977). "Conditional Confidence Statements and Confidence Estimators (with discussion)". Journal of the American Statistical Association. 72
Confidence_interval
Statistical hypothesis test
− m i ) 2 m i = ∑ i = 1 k x i 2 m i − n {\displaystyle X^{2}=\sum _{i=1}^{k}{\frac {(x_{i}-m_{i})^{2}}{m_{i}}}=\sum _{i=1}^{k}{{\frac {x_{i}^{2}}{m_{i}}}-n}}
Chi-squared_test
Graphical representation of the distribution of numerical data
Freedman, David; Diaconis, P. (1981). "On the histogram as a density estimator: L2 theory" (PDF). Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte
Histogram
Statistical model validation technique
PMID 25800943. Bengio, Yoshua; Grandvalet, Yves (2004). "No Unbiased Estimator of the Variance of K-Fold Cross-Validation" (PDF). Journal of Machine
Cross-validation_(statistics)
Statistical test
getting an asymptotically normal distribution after plugging in the MLE estimator of θ ^ {\displaystyle {\hat {\theta }}} into the SE relies on Slutsky's
Wald_test
Complete set of items that share at least one property in common
close to the population mean. Data collection system Horvitz–Thompson estimator Sample (statistics) Stratum (statistics) Bootstrap world Haberman, Shelby
Statistical_population
Numerical measure of a statistical relationship between variables
Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization
Correlation_coefficient
Test of normality in frequentist statistics
C=\left\|V^{-1}m\right\|={\left(m^{\mathsf {T}}V^{-1}V^{-1}m\right)}^{1/2}} and the vector m, m = ( m 1 , … , m n ) T {\displaystyle m=(m_{1},\dots ,m_{n})^{\mathsf
Shapiro–Wilk_test
Branch of statistics
unbiased estimators (UMVUE), sometimes called best unbiased estimators as well, are estimators that have minimum variance among all unbiased estimators. Due
Parametric_statistics
Statistical hypothesis test
Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization
F-test
Measure of covariance of components of a random vector
most often used estimators for the covariance matrices, but other estimators also exist, including regularised or shrinkage estimators, which may have
Covariance_matrix
Statistical method for handling multiple comparisons
m α ≤ α {\displaystyle E(Q)\leq {\frac {m_{0}}{m}}\alpha \leq \alpha } If an estimator of m 0 {\displaystyle m_{0}} is inserted into the BH procedure,
False_discovery_rate
Criterion for model selection
{1}{n}}\sum _{i=1}^{n}(x_{i}-{\widehat {x}}_{i})^{2}.} which is a biased estimator for the true variance. In terms of the residual sum of squares (RSS) the
Bayesian information criterion
Bayesian_information_criterion
Method of quality control
1069–76. ISBN 978-0-444-70077-3. Colosimo, Bianca M.; Jones-Farmer, L. Allison; Megahed, Fadel M.; Paynabar, Kamran; Ranjan, Chetan; Woodall, William
Statistical_process_control
Statistics term
X_{2})} is sufficient but not complete. It admits a non-zero unbiased estimator of zero, namely X 1 − X 2 {\textstyle X_{1}-X_{2}} . Most parametric models
Completeness_(statistics)
Correlation of a signal with a time-shifted copy of itself, as a function of shift
Markov theorem does not apply, and that OLS estimators are no longer the Best Linear Unbiased Estimators (BLUE). While it does not bias the OLS coefficient
Autocorrelation
Statistical principle
there is no sufficient statistic, although it is restricted to linear estimators. The Kolmogorov structure function deals with individual finite data;
Sufficient_statistic
Method of data analysis
is R ( k ) = ∑ j = k + 1 m σ j 2 ∑ j = 1 m σ j 2 {\displaystyle R(k)={\frac {\sum _{j=k+1}^{m}\sigma _{j}^{2}}{\sum _{j=1}^{m}\sigma _{j}^{2}}}} . The
Principal_component_analysis
Statistical model for count data
of m vectors x i ∈ R n + 1 , i = 1 , … , m {\displaystyle x_{i}\in \mathbb {R} ^{n+1},\,i=1,\ldots ,m} , along with a set of m values y 1 , … , y m ∈ N
Poisson_regression
Statistical technique correcting sampling bias
generalized, by Heckman and by others. The Heckman correction is a two-step M-estimator where the covariance matrix generated by OLS estimation of the second
Heckman_correction
Statistical property
errors all have the same variance. While the ordinary least squares (OLS) estimator is still unbiased in the presence of heteroscedasticity, it is inefficient
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Statistical interpretation with many tests
variables: m is the total number hypotheses tested m 0 {\displaystyle m_{0}} is the number of true null hypotheses, an unknown parameter m − m 0 {\displaystyle
Multiple_comparisons_problem
Type of chart
bar charts in Wikipedia, see Extension:EasyTimeline. Reimann, D.; Struwe, M.; Ram, N.; Gaschler, R. (2022). "Typicality effect in data graphs". Visual
Bar_chart
Statistics concept
squares. The least-squares method minimizes the variance of the unbiased estimators of the coefficients, under the conditions of the Gauss–Markov theorem
Polynomial_regression
Table that displays the frequency of variables
Models with Social Science Applications. North Holland, 1980. Bishop, Y. M. M.; Fienberg, S. E.; Holland, P. W. (1975). Discrete Multivariate Analysis:
Contingency_table
Estimator for quality of a statistical model
The Akaike information criterion (AIC) is an estimator of prediction error and thereby relative quality of statistical models for a given set of data
Akaike_information_criterion
Statistical modeling method
squares Line fitting Linear classifier Linear equation Logistic regression M-estimator Multivariate adaptive regression spline Nonlinear regression Nonparametric
Linear_regression
Measure of the joint variability
X 1 X 2 … X m ] T {\displaystyle \mathbf {X} ={\begin{bmatrix}X_{1}&X_{2}&\dots &X_{m}\end{bmatrix}}^{\mathrm {T} }} of m {\displaystyle m} jointly distributed
Covariance
Statistical method
Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model which is estimated
Bootstrapping_(statistics)
Single measure of some attribute of a sample
used for estimating a population parameter, the statistic is called an estimator. A population parameter is any characteristic of a population under study
Statistic
Statistical considerations on how many observations to make
confidence interval) this translates to a low target variance of the estimator. the use of a power target, i.e. the power of statistical test to be applied
Sample_size_determination
Linear regression model with a single explanatory variable
_{i=1}^{n}(x_{i}-{\bar {x}})^{2}}}}} is the unbiased standard error estimator of the estimator β ^ {\displaystyle {\widehat {\beta }}} . This t-value has a Student's
Simple_linear_regression
Statistical test comparing two probability distributions
n , m > c ( α ) n + m n ⋅ m . {\displaystyle D_{n,m}>c(\alpha ){\sqrt {\frac {n+m}{n\cdot m}}}.} Where n {\displaystyle n} and m {\displaystyle m} are
Kolmogorov–Smirnov_test
Sampling from a population which can be partitioned into subpopulations
n total individuals, m of which are male and f female (and where m + f = n), then the relative size of the two samples (x1 = m/n males, x2 = f/n females)
Stratified_sampling
Statistic measuring inter-rater agreement for categorical items
Intraclass correlation Krippendorff's alpha Statistical classification Banerjee, M.; Capozzoli, Michelle; McSweeney, Laura; Sinha, Debajyoti (1999). "Beyond
Cohen's_kappa
Study of collection and analysis of data
of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs
Statistics
Measure of distance between two proportions
Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization
Cohen's_h
Interpretation of probability
; Ahuacatzin, J.-M.; Mekhnacha, K. (2013). Bayesian Programming. CRC Press. ISBN 9781439880326. Bernardo, José M.; Smith, Adrian F.M. (1994). Bayesian
Bayesian_probability
Unit of information
ISSN 1545-7885. PMC 4640582. PMID 26556502. Gibson, Alexander D.; White, Nicole M.; Collins, Gary S.; Barnett, Adrian G. (4 June 2026). "Evidence of unreliable
Data
Conditional probability used in Bayesian statistics
New York: John Wiley & Sons. pp. 69–102. ISBN 0-471-63729-7. Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer. pp. 21–24
Posterior_probability
Plot using the dispersal of scattered dots to show the relationship between variables
pairs (X,Y), called a scatter diagram, frequently helps... Utts, Jessica M. Seeing Through Statistics 3rd Edition, Thomson Brooks/Cole, 2005, pp 166-167
Scatter_plot
Statistic which divides a data set into 100 parts and analyzes it as a percentage
Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization
Percentile
Statistical measure to determine how suited data is for factor analysis
1974. The measure of sampling adequacy is calculated for each indicator as M S A j = ∑ k ≠ j r j k 2 ∑ k ≠ j r j k 2 + ∑ k ≠ j p j k 2 {\displaystyle MSA_{j}={\frac
Kaiser–Meyer–Olkin_test
Fundamental theorem in probability theory and statistics
2007). Theorem—Let a martingale M n {\textstyle M_{n}} satisfy 1 n ∑ k = 1 n E [ ( M k − M k − 1 ) 2 ∣ M 1 , … , M k − 1 ] → 1 {\displaystyle {\frac
Central_limit_theorem
Nonparametric measure of rank correlation
Spearman's rank correlation coefficient estimator, to give a sequential Spearman's correlation estimator. This estimator is phrased in terms of linear algebra
Spearman's rank correlation coefficient
Spearman's_rank_correlation_coefficient
Diagnostic plot of binary classifier ability
calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function
Receiver operating characteristic
Receiver_operating_characteristic
Method used in statistics, pattern recognition, and other fields
Another strategy to deal with small sample size is to use a shrinkage estimator of the covariance matrix, which can be expressed mathematically as Σ =
Linear_discriminant_analysis
Function related to statistics and probability theory
maximum likelihood estimator. s n ( θ ) = 0 {\displaystyle s_{n}(\theta )=\mathbf {0} } In that sense, the maximum likelihood estimator is implicitly defined
Likelihood_function
Inverse of the average of the inverses of a set of numbers
log e ( x ) − m ) 2 {\displaystyle s^{2}={\frac {1}{n}}\sum \left(\log _{e}(x)-m\right)^{2}} Of these H3 is probably the best estimator for samples of
Harmonic_mean
Specialized form of regression analysis, in statistics
Iteratively reweighted least squares M-estimator Relaxed intersection RANSAC Repeated median regression Theil–Sen estimator, a method for robust simple linear
Robust_regression
Overview of and topical guide to statistics
Estimation theory Estimator Bayes estimator Maximum likelihood Trimmed estimator M-estimator Minimum-variance unbiased estimator Consistent estimator Efficiency
Outline_of_statistics
Statistical measure
x ) {\displaystyle f(x)\equiv f_{s=1}(x)} . An estimator of a scale parameter is called an estimator of scale. In the case where a parametrized family
Scale_parameter
Probabilistic problem-solving algorithm
{\displaystyle n\leq k} , then m k = m {\displaystyle m_{k}=m} ; sufficient sample simulations were done to ensure that m k {\displaystyle m_{k}} is within ϵ {\displaystyle
Monte_Carlo_method
Metric for fit of statistical models
Deviance (statistics) Overfitting Statistical model validation Theil–Sen estimator Berk, Robert H.; Jones, Douglas H. (1979). "Goodness-of-fit test statistics
Goodness_of_fit
M ESTIMATOR
M ESTIMATOR
Male
Egyptian
, the overseer of the sacrificiants of the temple of Amen.
Boy/Male
Indian
{h}name of Ganesh, {m}fire
Male
Spanish
Old Spanish form of Latin Abrahamus, ABRAÃM means "father of a multitude."Â
Boy/Male
Indian, Tamil
King of Kings; Pron; M Amannan
Male
Czechoslovakian
, resolute helmet.
Boy/Male
Indian
The self-existing by whom all subsist
Male
Hungarian
Hungarian form of Hebrew Adam, ÃDÃM means "earth" or "red."
Boy/Male
Muslim
The self-existing by whom all subsist
Boy/Male
Hindu
{h}lord Vishnu, {m}bright night
Girl/Female
Shakespearean
King Henry V' Earl of Salisbury.
Girl/Female
Muslim
The Arabic letter m, Mim (1)
Boy/Male
Tamil
{h}lord Vishnu, {m}bright night
Boy/Male
Hawaiian
M.
Male
Turkish
Turkish form of Hebrew Abraham, Ä°BRAHÄ°M means "father of a multitude."Â
Boy/Male
Tamil
{h}name of Ganesh, {m}fire
Boy/Male
Muslim
{h}name of Ganesh, {m}fire
Girl/Female
Muslim/Islamic
The Arabic letter 'M' or 'Mim'
Girl/Female
Indian
The Arabic letter m, Mim
Girl/Female
Assamese, Gujarati, Hindu, Indian, Kannada, Telugu
Holy Grains of Rice for Wedding Rituals
Surname or Lastname
English, German, Dutch, Polish, Slovenian, and Jewish; Hungarian (Ãbrám)
English, German, Dutch, Polish, Slovenian, and Jewish; Hungarian (Ãbrám) : from a reduced form of Abraham.English : habitational name from a place near Manchester, formerly Adburgham, named in Old English as ‘the homestead (Old English hÄm) of a woman called Ä’adburg’.
M ESTIMATOR
M ESTIMATOR
Girl/Female
Arabic, Muslim
Tender; Supple; Resilient
Girl/Female
Hindu
Script
Boy/Male
American, Australian, British, Christian, Danish, English, German, Teutonic
Son of Harry; God or Lord Vishnu
Girl/Female
Tamil
Narmada river
Boy/Male
Hindu
Destroyer of enemies
Boy/Male
Hindu, Indian
Sudha
Boy/Male
Muslim
Knowledge person, Wise, Scholarly, Omniscient, Learned
Boy/Male
Italian
God has shown favor.' See also Jovan.
Girl/Female
Indian, Tamil
Goddess of Beauty
Girl/Female
French, Hindu, Indian
Leaf
M ESTIMATOR
M ESTIMATOR
M ESTIMATOR
M ESTIMATOR
M ESTIMATOR
m.
A box for working implements; hence, a working outfit, as of a workman, a soldier, and the like.
m.
A group of separate parts, things, or individuals; -- used with whole, and generally contemptuously; as, the whole kit of them.
n. m.
A long-tailed falcon (Falco lanarius), of Southern Europe, Asia, and Northern Africa, resembling the American prairie falcon.
n.
Any plant of the genus Mertensia (esp. M. Virginica and M. Sibirica) plants nearly related to Pulmonaria. The American lungwort is Mertensia Virginica, Virginia cowslip.
n. m.
Alt. of Religieux
m.
The system, style, spirit, or character, of a priesthood, or sacerdotal order; devotion to the interests of the sacerdotal order.
m.
A large bottle.
n.
A French mastiff.
m.
straw or rush basket for fish; also, any kind of basket.
n.
A quadrat, the face or top of which is a perfect square; also, the size of such a square in any given size of type, used as the unit of measurement for that type: 500 m's of pica would be a piece of matter whose length and breadth in pica m's multiplied together produce that number.
n.
A symbol representing one thousand units; as, 1,000, M or CI/.
n. m.
A person bound by monastic vows; a nun; a monk.
n. m.
Alt. of Lanneret
n.
An Old World finch of the genus Minia, as the M. Malabarica of India, and M. cantans of Africa.
m.
A wooden tub or pail, smaller at the top than at the bottom; as, a kit of butter, or of mackerel.
n.
The sixtieth part of an hour; sixty seconds. (Abbrev. m.; as, 4 h. 30 m.)
n.
A brand or stigma, having the shape of an M, formerly impressed on one convicted of manslaughter and admitted to the benefit of clergy.
a.
Discovered or described by M. Tenon, a French anatomist.