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Truncated regression models are a class of models in which the sample has been truncated for certain ranges of the dependent variable. That means observations
Truncated_regression_model
Statistical model for censored regressands
In statistics, a tobit model is any of a class of regression models in which the observed range of the dependent variable is censored in some way. The
Tobit_model
These and other censored regression models are often confused with truncated regression models. Truncated regression models are used for data where whole
Censored_regression_model
Statistical modeling method
regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory variables (regressor
Linear_regression
Statistical model for count data
especially when used to model contingency tables. Negative binomial regression is a popular generalization of Poisson regression because it loosens the
Poisson_regression
Class of statistical models
linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be
Generalized_linear_model
In statistics, method of making values limited above or below
estimation of even moderately complicated models, such as regression models, for truncated data. In econometrics, truncated dependent variables are variables
Truncation_(statistics)
Statistical model for a binary dependent variable
independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model (the coefficients in
Logistic_regression
Class of statistical survival models
hazards model can itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which
Proportional_hazards_model
Statistics concept
polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as
Polynomial_regression
Bias in the sampling of a population
Sampling probability Selection bias Common source bias Spectrum bias Truncated regression model "Sampling Bias". Medical Dictionary. Archived from the original
Sampling_bias
Sub-class of survival models
word ‘regression’ in threshold regression refers to first-hitting-time models in which one or more regression structures are inserted into the model in order
First-hitting-time_model
Regression analysis technique
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is
Binomial_regression
Set of statistical processes for estimating the relationships among variables
non-linear models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis
Regression_analysis
Regression models accounting for possible errors in independent variables
contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only
Errors-in-variables_model
Statistical linear model
general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that
General_linear_model
Statistical model allowing for frequent zero values
"Poisson regression is traditionally conceived of as the basic count model upon which a variety of other count models are based." In a Poisson model, "… the
Zero-inflated_model
Regression analysis
nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters
Nonlinear_regression
experiment True variance Truncated distribution Truncated mean Truncated normal distribution Truncated regression model Truncation (statistics) Tsallis distribution
List_of_statistics_articles
Statistical regression where the dependent variable can take only two values
In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word
Probit_model
econometrics, the truncated normal hurdle model is a variant of the Tobit model and was first proposed by Cragg in 1971. In a standard Tobit model, represented
Truncated_normal_hurdle_model
Regression models that combine parametric and nonparametric models
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations
Semiparametric_regression
Mathematical model used for classification or regression
descent family) Examples of discriminative models include: Logistic regression, a type of generalized linear regression used for predicting binary or categorical
Discriminative_model
Concept in statistical analysis
{\displaystyle y} -intercept The least squares regression line is a method in simple linear regression for modeling the linear relationship between two variables
Bivariate_analysis
Branch of statistics
Cox models may be extended for such time-varying covariates. The Cox PH regression model is a linear model. It is similar to linear regression and logistic
Survival_analysis
model, ordered logit Multivariate probit models Probit, probit model, ordered probit Tobit model Censored regression model Selection bias Truncated regression
Limited_dependent_variable
Type of statistical model
term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression models and the term
Linear_model
Statistical technique
generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares
Total_least_squares
Empirical law on the variance of species in a habitat
results suggest that rather than a single regression line for the data set, a segmental regression may be a better model for genuinely random distributions.
Taylor's_law
Category of regression analysis
Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information
Nonparametric_regression
Type of machine learning model
evaluation, targeted preference-model reweighting, and multi-turn sycophancy benchmarks to measure persistence and regression risk.[citation needed] Industry
Large_language_model
Statistics concept
distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead
Errors_and_residuals
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Model for generating observable data in probability and statistics
they don't necessarily perform better than generative models at classification and regression tasks. The two classes are seen as complementary or as
Generative_model
Econometric term
time-invariance of regression coefficients − is a central issue in all applications of linear regression models. For linear regression models, the Chow test
Structural_break
Number of values in the final calculation of a statistic that are free to vary
regression methods, including regularized least squares (e.g., ridge regression), linear smoothers, smoothing splines, and semiparametric regression,
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Statistical method
parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y
Regression discontinuity design
Regression_discontinuity_design
Statistical hypothesis test
the data: here the restricted model uses all data in one regression, while the unrestricted model uses separate regressions for two different subsets of
F-test
Statistical phenomenon
In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where
Regression_toward_the_mean
Form of causal modeling that fit networks of constructs to data
each part of the model separately. Structural equation modeling (SEM) began differentiating itself from correlation and regression when Sewall Wright
Structural_equation_modeling
Concept in statistics
models from the classical exponential family, and include 3 of the most important statistical regression models: the linear model, Poisson regression
Vector generalized linear model
Vector_generalized_linear_model
Statistical technique correcting sampling bias
ISBN 0-520-04723-0. Breen, Richard (1996). Regression Models : Censored, Sample Selected, or Truncated Data. Thousand Oaks: Sage. pp. 33–48. ISBN 0-8039-5710-6
Heckman_correction
Branch of statistics mathematics
classification models, functional generalized linear models or more specifically, functional binary regression, such as functional logistic regression for binary
Functional_data_analysis
Specialized form of regression analysis, in statistics
statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between
Robust_regression
Overview of and topical guide to machine learning
(SOM) Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)
Outline_of_machine_learning
Branch of statistics
carrying out regression analysis have been developed. Familiar methods, such as linear regression, are parametric, in that the regression function is defined
Mathematical_statistics
Approximation method in statistics
In regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—the differences between
Least_squares
Parametric model in survival analysis
the survival model, the regression parameter estimates from AFT models are robust to omitted covariates, unlike proportional hazards models. They are also
Accelerated failure time model
Accelerated_failure_time_model
Branch of statistics
(X_{1},Y_{1}),\dots ,(X_{n},Y_{n})} and a regression function f {\displaystyle f} is to be determined. The model parameters are chosen such that the sum
Parametric_statistics
Estimator for quality of a statistical model
BIC in the context of regression is given by Yang (2005). In regression, AIC is asymptotically optimal for selecting the model with the least mean squared
Akaike_information_criterion
In probability, a theory
bias. In a first step, a regression for observing a positive outcome of the dependent variable is modeled with a probit model. The inverse Mills ratio
Mills_ratio
In statistical modeling (especially process modeling), polynomial functions and rational functions are sometimes used as an empirical technique for curve
Polynomial and rational function modeling
Polynomial_and_rational_function_modeling
Condition in which the value of a measurement or observation is only partially known
time-of-test-termination for those that did not fail. An earlier model for censored regression, the tobit model, was proposed by James Tobin in 1958. The likelihood
Censoring_(statistics)
sample selection problem. This selection issue is akin to the truncated regression model where we face selection on the basis of a binary response variable
Stock_sampling
Statistical model validation technique
context of linear regression is also useful in that it can be used to select an optimally regularized cost function.) In most other regression procedures (e
Cross-validation_(statistics)
Tools to represent statistical uncertainty
probability function. Confidence bands commonly arise in regression analysis. In the case of a simple regression involving a single independent variable, results
Confidence and prediction bands
Confidence_and_prediction_bands
Task of selecting a statistical model from a set of candidate models
for models with high parameter spaces. Extended Fisher Information Criterion (EFIC) is a model selection criterion for linear regression models. Constrained
Model_selection
Type of numerical analysis
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations
Isotonic_regression
Distributional regression model
The generalized additive model for location, scale and shape (GAMLSS) is a distributional regression model in which a parametric statistical distribution
Generalized additive model for location, scale and shape
Generalized_additive_model_for_location,_scale_and_shape
Mathematical model for stochastic processes
Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included, are
Generalized functional linear model
Generalized_functional_linear_model
General linear model that blends ANOVA and regression
Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable
Analysis_of_covariance
Family of functions to transform data
follows a truncated normal distribution, then Y is said to follow a Box–Cox distribution. Bickel and Doksum eliminated the need to use a truncated distribution
Power_transform
Statistical measure of the magnitude of a phenomenon
sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, and the risk of a particular event
Effect_size
Statistical term
dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation
Path_analysis_(statistics)
Normality test
David Lilien, et al. (1995) when using this test along with multiple regression analysis the right estimate is: J B = n − k 6 ( S 2 + 1 4 ( K − 3 ) 2
Jarque–Bera_test
Sequence of data points over time
called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial that models the entire
Time_series
Type of statistics
their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between
Robust_statistics
Time series model
proposed the use of Gaussian process regression models to obtain a GARCH scheme. This results in a nonparametric modelling scheme, which allows for: (i) advanced
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
estimate the model contain values that change sign, or if the lowest response value is far from zero (for example, when data are left-truncated), a location
Response_modeling_methodology
Method of estimating the parameters of a statistical model, given observations
analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random errors are assumed to have
Maximum_likelihood_estimation
Probabilistic classification algorithm
predicted by the linear model that underlies logistic regression. Since naive Bayes is also a linear model for the two "discrete" event models, it can be reparametrised
Naive_Bayes_classifier
Study of high-dimensional data
structure. One common assumption for high-dimensional linear regression is that the vector of regression coefficients is sparse, in the sense that most coordinates
High-dimensional_statistics
Artificial intelligence concept
containing sorting errors, simply truncated the list. Another of GenProg's misaligned strategies evaded a regression test that compared a target program's
Reward_hacking
Design of tasks
publication on an optimal design for regression models in 1876. A pioneering optimal design for polynomial regression was suggested by Gergonne in 1815.
Design_of_experiments
Graph that misrepresents data
was truncated, they still overestimated the actual differences, often substantially. These graphs display identical data; however, in the truncated bar
Misleading_graph
Type of regression analysis
Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified
Functional_regression
Japanese economist (1935–2026)
Takeshi (1974). "Multivariate Regression and Simultaneous Equation Models when the Dependent Variables Are Truncated Normal". Econometrica. 42 (6): 999–1012
Takeshi_Amemiya
Approximation method in statistics
the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x ,
Non-linear_least_squares
Statistics concept
multiple regression analysis or causal modelling. To quantify the effect of a moderating variable in multiple regression analyses, regressing random variable
Moderation_(statistics)
Statistical hypothesis test
the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4. From the regression, the slope
Student's_t-test
Concept in machine learning
with larger models. Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise. A model of double descent
Double_descent
Method used in statistics, pattern recognition, and other fields
categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain
Linear_discriminant_analysis
How many standard deviations apart from the mean an observed datum is
to multiple regression analysis is sometimes used as an aid to interpretation. (page 95) state the following. "The standardized regression slope is the
Standard_score
Diagnostic plot of binary classifier ability
for regression problems are the so-called regression error characteristic (REC) Curves and the Regression ROC (RROC) curves. In the latter, RROC curves
Receiver operating characteristic
Receiver_operating_characteristic
Statistical hypothesis test for forecasting
are retained in the regression. Multivariate Granger causality analysis is usually performed by fitting a vector autoregressive model (VAR) to the time
Granger_causality
Statistical method
testing. In regression problems, case resampling refers to the simple scheme of resampling individual cases – often rows of a data set. For regression problems
Bootstrapping_(statistics)
Statistical property
special case of testing within regression models, some tests have structures specific to this case. Tests in regression Goldfeld–Quandt test Park test
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Categorization of data using statistics
algorithms include Logistic regression – Statistical model for a binary dependent variable Multinomial logistic regression – Regression for more than two discrete
Statistical_classification
Statistical hypothesis test for the presence of serial correlation
autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic
Breusch–Godfrey_test
Overview of and topical guide to statistics
Survivorship bias Regression analysis Outline of regression analysis Analysis of variance (ANOVA) General linear model Generalized linear model Generalized
Outline_of_statistics
Metric for fit of statistical models
Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness
Goodness_of_fit
Study of uncertainty in the output of a mathematical model or system
and output variables. Regression analysis, in the context of sensitivity analysis, involves fitting a linear regression to the model response and using standardized
Sensitivity_analysis
Iterative method for finding maximum likelihood estimates in statistical models
underlying linear regression model exists explaining the variation of some quantity, but where the values actually observed are censored or truncated versions
Expectation–maximization algorithm
Expectation–maximization_algorithm
Criterion for model selection
{\displaystyle k} = the number of parameters estimated by the model. For example, in multiple linear regression, the estimated parameters are the intercept, the q
Bayesian information criterion
Bayesian_information_criterion
Family of statistical methods based on sampling of available data
uses the sample median; to estimate the population regression line, it uses the sample regression line. It may also be used for constructing hypothesis
Resampling_(statistics)
Statistical matching technique
a control in regression, or by matching on the confounding variable. PSM has been shown to increase model "imbalance, inefficiency, model dependence, and
Propensity_score_matching
Type of statistical model
LS method was recommended by Speckman. Kernel regression also was introduced in partially linear model. The local constant method, which is developed
Partially_linear_model
Measure of linear correlation
Standardized covariance Standardized slope of the regression line Geometric mean of the two regression slopes Square root of the ratio of two variances
Pearson correlation coefficient
Pearson_correlation_coefficient
Simultaneous observation and analysis of more than one outcome variable
problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate
Multivariate_statistics
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Surname or Lastname
English and Dutch
English and Dutch : from the medieval personal name Benedict (Latin Benedictus meaning ‘blessed’). This owed its popularity in the Middle Ages chiefly to St. Benedict of Norcia (c.480–550), who founded the Benedictine order of monks at Monte Cassino and wrote a monastic rule that formed a model for all subsequent rules. No doubt the meaning of the Latin word also contributed to its popularity as a personal name, especially in Romance countries.
Surname or Lastname
English
English : variant of Ayliff(e), which is from a Middle English personal name. In most cases, this is Old Norse EilÃfr ‘eternal life’, but it could also have absorbed the female name Ayleve (Old English Æ{dh}elgifu ‘noble gift’). It could also have absorbed a truncated form of Irish McAuliffe.
Surname or Lastname
English (chiefly West Midlands)
English (chiefly West Midlands) : nickname for a trustworthy person, from Middle English trow(e), trew(e) ‘faithful’, ‘steadfast’.English : variant of Tree, from Middle English trow, trew.English : topographic name for someone who lived near a depression in the ground, from Middle English trow ‘trough’, ‘hollow’.Translated form of French Jetté (see Jette). Trow represents the French Canadian pronunciation of English ‘throw’.
Boy/Male
Muslim
Sample, Model, Paragon
Surname or Lastname
English and French
English and French : nickname for a tall person, from Old English lang, long, Old French long ‘long’, ‘tall’ (equivalent to Latin longus).Irish (Ulster (Armagh) and Munster) : reduced Anglicized form of Gaelic Ó Longáin (see Langan).Chinese : from the name of an official treasurer called Long, who lived during the reign of the model emperor Shun (2257–2205 bc). his descendants adopted this name as their surname. Additionally, a branch of the Liu clan (see Lau 1), descendants of Liu Lei, who supposedly had the ability to handle dragons, was granted the name Yu-Long (meaning roughly ‘resistor of dragons’) by the Xia emperor Kong Jia (1879–1849 bc). Some descendants later simplified Yu-Long to Long and adopted it as their surname.Chinese : there are two sources for this name. One was a place in the state of Lu in Shandong province during the Spring and Autumn period (722–481 bc). The other source is the Xiongnu nationality, a non-Han Chinese people.Chinese : variant of Lang.Cambodian : unexplained.
Surname or Lastname
English and Irish (of Norman origin), and northern French
English and Irish (of Norman origin), and northern French : habitational name from any of several places in northern France, such as Nogent-sur-Oise, named with Latin Novientum, apparently an altered form of a Gaulish name meaning ‘new settlement’.The Anglo-Norman family of this name is descended from Fulke de Bellesme, lord of Nogent in Normandy, who was granted large estates around Winchester after the Conquest. His great-grandson was Hugh de Nugent (died 1213), who went to Ireland with Hugh de Lacy, and was granted lands in Bracklyn, County Westmeath. The family formed itself into a clan on the Irish model, of which the chief bore the hereditary title of Uinsheadun (Irish Uinnseadún), from their original seat at Winchester. They have been Earls of Westmeath since 1621. The name is now a common one in Ireland, and has been adopted there by some who have no connection with the clan.
Surname or Lastname
German
German : habitational name from any of several places so named, for example in Westphalia and Switzerland.German : nickname from Middle High German heiden ‘heathen’, Old High German heidano, apparently a derivative of heida ‘heath’, modeled on Latin paganus (see Pain 1). The nickname was sometimes used to refer to a Christian knight who had been on a Crusade to fight in the Holy Land.Jewish (Ashkenazic) : of uncertain origin; possibly a shortened form of any of various ornamental names formed with German Heide- ‘heath’, for example Heidenberg, Heidenkorn, Heidenkrug, Heidenwurzel.English : variant spelling of Hayden.Dutch : shortened form of vanderHeiden.
Boy/Male
Arabic, Muslim
Leadership; Individuality; Aggression; Self-confidence; Originality; Impatience.
Surname or Lastname
English
English : from a medieval personal name, a short form of Philpott.English : topographic name for someone who lived by a depression in the ground, from Middle English pot ‘drinking or storage vessel’ used in this transferred sense, or a habitational name from one of the minor places deriving their name from this word, in the sense ‘pit’, ‘hole’.English and North German (Lower Rhine-Westphalia) : metonymic occupational name for a potter, from Middle English, Middle Low German pot ‘pot’. See also Potter.North German : topographic name for someone living on a low-lying plot, from Low German dialect pÅt ‘puddle’.
Surname or Lastname
English (chiefly West Midlands)
English (chiefly West Midlands) : from the Middle English personal name Myat, formed from My, a truncated version of Mihel (an Old French form of Michael) + the diminutive suffix -at (from Old French -et, crossed with the originally pejorative Old French -ard).
Boy/Male
Muslim
Model, Example
Male
Greek
(Καϊάφας) Greek form of Aramaic Qayyafa ("depression"), KAIAPHAS means "as comely." In the New Testament bible, this is the name of a high priest of the Jews.Â
Surname or Lastname
English (West Midlands)
English (West Midlands) : habitational name from any of the places called Harthill, named with Old English heorot ‘hart’ + hyll ‘hill’. There are several places of this name, for example in Cheshire, Derbyshire, and South Yorkshire, but apparently none in the West Midlands. It is also possible that the surname represents a truncated derivative of Hartlebury in Worcestershire. This place name derives from the Old English personal name Heortla + Old English burh ‘fort’.German : Americanized spelling of Hartel or Härtel.
Surname or Lastname
English (mainly southwest England)
English (mainly southwest England) : topographic name for someone who lived by a depression or low-lying spot, from Old English holh ‘hole’, ‘hollow’, ‘depression’.Norwegian : habitational name from any of numerous farmsteads, so named from the dative singular or indefinite plural form of Old Norse hóll ‘round hill’, ‘mound’.Shortened form of Dutch van (den) Hole, a habitational name from the common place name Hol, meaning ‘hollow’, ‘depression’, ‘valley’, or a topographic name from the same term.
Girl/Female
Czech, Czechoslovakian, Danish, Finnish, German, Hebrew, Irish, Jewish, Polish
Friend; Beautiful; Model of Righteous Convert; Friendship
Surname or Lastname
English and Scottish
English and Scottish : occupational name for a stonemason, Middle English, Old French mas(s)on. Compare Machen. Stonemasonry was a hugely important craft in the Middle Ages.Italian (Veneto) : from a short form of Masone.French : from a regional variant of maison ‘house’.George Mason (1725–92), the American colonial statesman who framed the VA Bill of Rights and Constitution, which was used as a model by Thomas Jefferson when drafting the Declaration of Independence, was a VA planter, fourth in descent from George Mason (?1629–?86), a royalist soldier of the English Civil War who had received land grants in VA. As well as being prominent in the affairs of VA, the family also produced the first governor of MI.
Surname or Lastname
English (Yorkshire and Lancashire)
English (Yorkshire and Lancashire) : topographic name for someone who lived by a depression or low-lying spot, from Old English holh ‘hole’, ‘hollow’, ‘depression’ (see Hole).Irish : reduced Anglicized form of Gaelic Mac Giolla Chomhghaill, a patronymic from a personal name meaning ‘devotee of (Saint) Comhghal’ (see McCool). Woulfe, however, traces Hoyle (as well as MacIlhoyle and McElhill) to Mac Giolla Choille ‘son of the lad of the wood’, which has sometimes been translated as Woods.
Boy/Male
Hindu
Model state of india
Surname or Lastname
English and French
English and French : topographic name from Middle English, Old French court(e), curt ‘court’ (Latin cohors, genitive cohortis, ‘yard’, ‘enclosure’). This word was used primarily with reference to the residence of the lord of a manor, and the surname is usually an occupational name for someone employed at a manorial court.English : nickname from Old French, Middle English curt ‘short’, ‘small’ (Latin curtus ‘curtailed’, ‘truncated’, ‘cut short’, ‘broken off’).Irish : reduced form of McCourt.
Boy/Male
Egyptian
To model.
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Boy/Male
Hebrew, Hindu, Indian, Italian
God will Help
Girl/Female
Hindu
King of stars, Map
Boy/Male
Hindu
The false pride
Biblical
the city of victory
Female
English
Variant spelling of English Cindy, SINDY means "woman from Kynthos."Â
Boy/Male
Bengali, Hindu, Indian
King; Prince
Boy/Male
Hindu, Indian, Sanskrit
Cool Rayed
Girl/Female
British, English
Blessed One
Boy/Male
Assamese, Hindu, Indian, Marathi, Mythological, Sanskrit
A Character of Mahabharata; Son of King Shantanu
Boy/Male
Australian, French
Son of William
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n.
The first attack, or act of hostility; the first act of injury, or first act leading to a war or a controversy; unprovoked attack; assault; as, a war of aggression. "Aggressions of power."
n.
Aggression.
a.
Lacking the apex; -- said of certain spiral shells in which the apex naturally drops off.
a.
Having the edges truncated.
a.
Growing broader and broader, as a leaf; truncate.
p. pr. & vb. n.
of Truncate
a.
Alt. of Tunicated
n.
The act of repressing, or state of being repressed; as, the repression of evil and evil doers.
n.
A large truncated cone of refined sugar.
a.
Reduced to a stub; short and thick, like something truncated; blunt; obtuse.
a.
Appearing as if cut off at the tip; as, a truncate leaf or feather.
a.
Replaced, or cut off, by a plane, especially when equally inclined to the adjoining faces; as, a truncated edge.
n.
The state of being truncated.
n.
The act of ceding back; restoration; repeated cession; as, the recession of conquered territory to its former sovereign.
imp. & p. p.
of Truncate
n.
Digression.
v. t.
To lop off; to curtail; to truncate; to maim.
n.
The act of passing back or returning; retrogression; retrogradation.
a.
Covered with a tunic; covered or coated with layers; as, a tunicated bulb.
a.
Cut off; cut short; maimed.